Thursday, July 24, 2025

TextureConsume - Texture Consume & Texture Emit, Creative handling of texture & SVG Polygon handles by Rupert S 2025

Texture Consume & Texture Emit, Creative handling of texture & SVG Polygon handles by Rupert S 2025


Intended to reduce latency of the following examples, Mice pointer sprites, Icons, Fonts, packed layer & flattened polygon meshes for example SVG Polygon images

"I thought of another one..

Maybe the streaming application could use the property : Consume Texture on WebASM & JS,..

Maybe more of the JS & WebASM could use Emit texture & Consume Texture, Those would probably work!

JewelsOfHeaven Latency Reducer for Mice pointers, Icons & Textures of simple patterns & frames,..

Emit Texture + Consume Texture, Most of the time a mouse pointer barely changes..

So we will not only Consume Texture but also store the texture in RAM Cache, If Sprites are non ideal & that is to say not directly GPU & screen surface handled,..

We can save the textures to a buffer on the GPU surface, Afterall fonts will do the same & store a sorted Icon / Polygon rendering list,

We can save static frames in the rendering list & animate in set regions,.. Consume Buffer Texture Makes sense..

Cool isn't it the C920 still being popular with models..

"

Texture Consume & Texture Emit, Creative handling of texture & SVG Polygon handles,

Intended to reduce latency of the following examples, Mice pointer sprites, Icons, Fonts, packed layer & flattened polygon meshes for example SVG Polygon images

By the direct emission of meta data such as location & depth data in relation to a layered render UI

Properties Metadata list

Location
Depth
Size

Other properties such as colour shift & palette

Intended content

Vectors
Fonts
Textures, & Such as Pre rendered Fonts by word or by letter
Flattened SVG Vectors & Texture converted SVG Vectors
Png, Gif, icon, JPG & movie 16x16 compressed pixel groups

Right, once we have saved a group of Compressed Polygons, Flattened, Texture converted, Layered or texture animation frames such as Png, Gif, icon, JPG & movie 16x16 compressed pixel groups,

We emit location properties ( a regular part of rendering ),

Store a Texture Buffer

Commit texture emit from Source UI or API

Texture Consume on the GPU rendering pipeline

Example function

Mouse pointer handler DLL & Mouse Driver,

Location of the pointer is set by the driver emitting path data for the mouse pointer,..

Emission of context related Sprite Texture or Vector SVG is handled by 2 paths:

Small DLL from the driver emits a location beacon & properties such as click & drag,

Handling location data & operations..

Screen renderer, OpenCL, OpenGL, Vulkan, DirectX, SDL

Operating System UI or renderer API, Cad & games or utilities interacts with screen renderer, OpenCL, OpenGL, Vulkan, DirectX, SDL

The result is a cycle of Metadata enabled texture emission & consume cycles..

The resulting operations should be fast

(c)Rupert S

*

This proposed system aims to reduce latency in rendering common UI elements like mouse pointers, icons, fonts, and SVG polygons by creating a more direct and efficient communication channel between the application (the "emitter") and the GPU (the "consumer").

Core Concepts of the Proposal

The central idea revolves around two main actions:

Texture Emit: This would be the process where a source application, JavaScript/WebAssembly code, or even a driver-level component sends not just the texture data itself,..

But also a packet of "metadata." This metadata would include essential rendering information like position (location), layering (depth), and size directly.

Texture Consume: This represents the GPU's rendering pipeline directly receiving and processing this combined texture and metadata packet.

The GPU would use this information to place and render the texture without needing as much intermediate processing by the CPU or the graphics driver's main thread.

How It Proposes to Reduce Latency

The proposal suggests that for frequently updated but often visually static elements like a mouse cursor, significant performance gains can be achieved.

Caching on the GPU: The system would store frequently used textures (like the standard pointer, a clicked pointer, or a loading spinner) directly in the GPU's VRAM.

This is referred to as a "Texture Buffer" or "RAM Cache"..

Minimizing Data Transfer: Instead of re-sending the entire texture for every frame or every small change, the application would only need to "emit" a small packet of metadata.

For a mouse pointer, this would simply be the new X/Y coordinates..

The GPU would then "consume" this location data and render the already-cached texture in the new position.

Direct Driver/API Interaction: The idea extends to having low-level components, like a mouse driver's DLL, emit location data directly to the graphics pipeline.

This could potentially bypass layers of the operating system's UI composition engine, further reducing latency.

*

Overview:

This model introduces two core operations:

Emit Texture: package and send pre-processed texture or vector data along with metadata.

Consume Texture: retrieve and bind textures efficiently from GPU-resident buffers.

The goal is to minimize CPU–GPU synchronization stalls by keeping mostly static assets cached on the GPU and updating only changed regions.

DComp texture support : Media Foundation Inclusions:

https://chromium.googlesource.com/chromium/src/+/refs/tags/134.0.6982.1/ui/gl/dcomp_surface_registry.h

Key Concepts:

Texture Emit & Texture Consume

A low-latency approach for handling sprites, icons, fonts, and flattened SVG meshes in modern rendering pipelines.

Metadata Beacon:

location: screen coordinates or world-space position

depth: z-order or layer index

size: width, height or scale factors

extra: colour shift, palette index, animation frame

Asset Types & Preparation:

pre-rasterized fonts (per-letter or per-word)

Single glyphs (per letter) or glyph clusters (per word).

Flattened SVG Vectors : Flattened SVG paths converted to textures

Paths baked into 8-bit or 16-bit alpha bitmaps.

Sprite & Icon Sheets

packed icon and sprite sheets

Packed 16×16, 32×32, or variable-size atlases.

Compressed Frame Groups

compressed 16×16 frames : Tiny Texture/GIF/WebP/PNG/JPEG sequences or video thumbnails.

Emit Phase

Source (app, JS/WebAssembly module, or driver DLL) packages a preprocessed bitmap or vector-derived texture.

UI or driver emits a texture packet containing compressed pixel data or vector-derived bitmap.

Include metadata beacon for placement and layer ordering.

Appends a metadata beacon containing placement, layering, scale, and optional modifiers.

GPU Caching

On first use, upload packet to a persistent GPU texture buffer.

Store a handle (texture ID + region) in a lookup table.

Consume Phase

Renderer fetches the handle, binds the buffer, and issues draw calls using metadata.

If region is static, skip re-upload and reuse existing GPU resource.

A lightweight DLL or driver extension emits pointer location and state beacons.

Renderer (OpenGL, Vulkan, DirectX, WebGPU) binds the GPU buffer and draws quads at specified positions.

Consume Texture

Each frame, the renderer binds the cached handle and issues draw calls using only updated metadata.

Static regions skip re-upload; only small metadata updates traverse the CPU–GPU bus.

Benefits

Reduced data transfers by caching static textures on GPU.

Minimal per-frame CPU workload: only metadata updates for mostly unchanging UI elements.

Consistent pipeline whether handling sprites, fonts, or complex vector meshes.

Next Steps

Build a minimal native plugin for Vulkan and OpenGL.

Prototype a WebAssembly module exposing the API to JS.

Define a small WebAssembly module exposing emit/consume calls to JavaScript-based UIs.

Integrate with a dummy mouse-driver DLL to emit pointer metadata.

Browser & Sandbox Integration

Map emitTexture/consumeTexture to WebGPU bind groups and dynamic uniform buffers.

Constrain direct driver hooks to browser-approved extensions or WebGPU device labels.

Enforce same-origin and content-security-policy checks for metadata beacons.

Investigate region-based dirty-rect optimizations to further trim uploads.

Benchmark cursor latency against traditional sprite-sheet approaches.

Benchmark against existing sprite-sheet and font-atlas approaches for pointer and icon latency.

Explore region-based dirty-rect tracking to further reduce draw calls.

//basics

// WebAssembly & JavaScript Binding

Module Exports

export function emitTexture(ptr: number, len: number, metaPtr: number): number;

export function consumeTexture(handle: number, metaPtr: number): void;

// WebAssembly / Native Interface

uint32_t emitTexture(uint8_t* pixelData, size_t bytes,
MetadataBeacon meta, EmitOptions opts);

void consumeTexture(uint32_t handle, MetadataBeacon meta);

void evictTexture(uint32_t handle);
size_t queryVRAMUsage();

//C WebAssembly Compatible

// Upload & retrieve a handle

uint32_t emitTexture(
const void* pixelData,
size_t byteLength,
MetadataBeacon meta,
EmitOptions opts

);

// Draw a previously emitted texture

void consumeTexture(
uint32_t handle,
const MetadataBeacon& meta

);

// Free VRAM when no longer needed

void evictTexture(uint32_t handle);

// Query total and used VRAM for diagnostics

size_t queryVRAMUsage();

RS

*

Review of “Texture Emit & Texture Consume” Proposal


Summary of Core Ideas

The proposal outlines a two-step workflow for ultra-low-latency UI rendering:

Emit Texture An application or driver packages up a pre-processed texture (sprite, icon, font glyph or flattened SVG) together with a small “metadata beacon” containing position, depth, size and optional attributes (colour shift, animation frame, palette index).

Consume Texture The GPU pipeline binds and renders from a persistent texture buffer on VRAM, using only the updated metadata beacon each frame rather than reuploading full bitmaps.

This approach caches static or semi-static assets directly on the GPU, minimizes CPU–GPU round trips, and can even let a tiny mouse-driver DLL send pointer coordinates straight into the rendering API.

Strengths

Reduces per-frame texture uploads to simple metadata updates

Leverages VRAM caching to minimize CPU–GPU synchronization stalls

Applies uniformly to cursors, icons, pre-rasterized fonts, spritesheets, flattened SVGs

Can bypass heavy OS composition layers via direct driver/API hooks

Fits within modern APIs (OpenGL, Vulkan, DirectX, WebGPU, even WebAssembly)

Potential Challenges

VRAM Management Storing many cached textures risks running out of GPU memory—would need eviction policies and size quotas.

Cross-Platform Consistency Different drivers and OSes expose different low-level hooks..

Abstracting a uniform “emit/consume” API may require shims per platform.

Security & Sandbox Browser environments (WebAssembly/JS) typically forbid arbitrary driver extensions..

Would need WebGPU or a secure binding layer.

Metadata Bandwidth vs. Texture Size For very small UI assets (16×16 cursors), metadata is tiny..

But if an app sends larger bitmaps frequently, the advantage diminishes.

Implementation Roadmap

Define a Minimal API

WebAssembly exports emit Texture(handle, metadata) and consume Texture(handle, metadata).

Native side maps handles to GPU buffers.

Prototype in a Graphics Framework

Build a DLL/plugin for OpenGL or Vulkan that registers new commands.

Hook the mouse driver to call emit Texture on pointer moves.

Memory & Eviction Strategy

Implement LRU caching of textures in VRAM.

Expose a query to evict unused assets under pressure.

Browser Integration

Use WebGPU’s buffer and texture binding model to replicate the pipeline in JS/WebAssembly.

Ensure this sits safely inside the web sandbox.

Benchmark & Iterate

Compare end-to-end cursor latency against classical sprite-sheet or atlas-based techniques.

Measure CPU usage savings when rendering dynamic UIs with many icons or glyphs.

RS

*

*Reference content*>

Logitech C920 has internal codecs 2012 (c)RS

Logitech C920 has internal codecs, Now logitech thinks.. Why waste space on internal codecs,

But you see webcams with internal codecs produce a texture (as described by microsoft on the about:features page search for GPU on the page, input about:features in the page entry at the top),

Sorry not everyone is used to using the about:about pages..

Now when the codec in cam produces a texture that is one thing less for the webcam process to perform when you are live streaming in the browser!

I thought of another one,

Maybe the streaming application could use property : Consume Texture on WebASM & JS,..

Maybe more of the JS & WebASM could use Emit texture & Consume Texture, Those would probably work!

JewelsOfHeaven Latency Reducer for Mice pointers, Icons & Textures of simple patterns & frames,..

Emit Texture + Consume Texture, Most of the time a mouse pointer bearly changes..

So we will not only Consume Texture but also store the texture in RAM Cache, If Sprites are non ideal & that is to say not directly GPU & screen surface handled,..

We can save the textures to a buffer on the GPU surface, After-all fonts will do the same & store a sorted Icon / Polygon rendering list,

We can save static frames in the rendering list & animate in set regions,.. Consume Buffer Texture Makes sense..

Cool isn't it the C920 still being popular with models..

https://is.gd/TV_GPU25_6D4

https://is.gd/AboutWebcam

Why Hardware Codecs Matter in Webcams

When your webcam has a built-in H.264 (or MJPEG) encoder, it hands off raw sensor data to a tiny onboard ASIC instead of burdening your PC’s CPU.

The result? Lower latency, less frame drops, and power savings—especially critical when you’re live-streaming in a browser.

Benefits of Onboard Compression

Offloads real-time encoding from your CPU

Produces a GPU-ready texture, enabling zero-copy rendering

Reduces memory bandwidth (no huge YUY2 frames flying over USB)

Lowers overall system latency and power draw

How Browsers Leverage Encoded Streams

Modern browsers expose H.264–encoded camera feeds through the Media Foundation Video Capture (Windows) or native UVC stack (macOS/Linux). Instead of:

USB forum-compliant YUY2 → CPU decode → GPU upload

CPU encode → network

you get:

USB → H.264 → GPU-side decoder → WebGL/WebRTC texture

This bypasses extra copies and CPU work, so frames hit your stream pipeline faster.

Logitech C920 in 2025: Still Going Strong

Logitech’s C920 was among the first sub-€100 webcams with hardware H.264. Its lasting popularity comes down to:

Reliable UVC implementation across OSes

Smooth 1080p30 H.264 with MJPEG/YUY2 fallback

Wide driver support in browsers and streaming apps

Feature

C920
Hardware Codec UVC H.264, MJPE, YUY2
~€70

C922
Hardware Codec UVC H.264, MJPE, YUY2
~€80

Brio 4K
Hardware Codec UVC H.264, HEVC, YUY2
~€150

WebCodecs API: Direct access to encoder/decoder in browser JavaScript

UVC 1.5 & HEVC cams: 10-bit, HDR, even hardware VP9/AV1 on emerging models

GPU-accelerated filters: Offload color correction or noise reduction to your GPU

*

Unlocking Next-Gen Webcam Pipelines


Below we’ll dive into three pillars for ultra-efficient, high-quality live streaming right in your browser.

1. WebCodecs API: Native Encoder/Decoder Access

With WebCodecs, you skip glue code and tap directly into hardware or software encoders and decoders from JavaScript.

Expose video encoder/decoder objects via promises

Feed raw Videoframe buffers into an Video Encoder

Receive compressed chunks (H.264, VP8, AV1) ready for RTP or Web Transport

Drastically lower latency compared to MediaRecorder or CanvasCaptureStream

Key considerations:

Browser support varies; Chrome and Edge lead the pack, Firefox is experimenting

You manage codec parameters (bitrate, GOP length) frame by frame

Integration with WebAssembly for custom pre-processing

2. UVC 1.5 & HEVC-Capable Cameras

USB Video Class 1.5 expands on classic UVC 1.1/1.5 to bring HDR, 10-bit color, and modern codecs into commodity webcams.

Supports hardware HEVC (H.265) encoding at up to 4K30

Enables true 10-bit per channel colour and HDR formats like HLG and PQ

Emerging models even integrate VP9 or AV1 encoders for streaming in browsers

Backward-compatible fallbacks: MJPEG or YUY2 when HEVC isn’t supported

Why it matters:

HDR and 10-bit eliminate banding in gradients and night scenes

HEVC and AV1 improve compression efficiency by 30-50% over H.264

Reduces CPU load even further when paired with WebCodecs or MSE

3. GPU-Accelerated Filters

Offload pixel-level work—denoising, colour correction, sharpening—directly onto your GPU for zero impact on the CPU.

Use WebGL/WebGPU to run shaders on each incoming frame (raw or decoded)

Chain filter passes: temporal denoise → auto-exposure → color LUT → sharpening

Leverage libraries like TensorFlow.js with WebGPU backends for AI-driven enhancement

Maintain 60 fps even on modest GPUs by optimizing shader complexity and texture formats

Best practices:

Do initial frame down-sampling for heavy noise reduction, then upscale

Use ping-pong render targets to minimize texture uploads

Profile with the browser’s GPU internals page (edge://gpu or chrome://gpu)

What’s Your Ideal Pipeline?

Do you want to see a sample WebCodecs implementation, pick a UVC 1.5 cam model, or deep-dive into filter shader code? Let me know—happy to drill into whichever piece you’re building next.

Further Reading & Exploration

Web Transport for low-latency transport of your encoded frames

AV1 Realtime Profiles: hardware boards vs. software fallbacks

Hybrid CPU/GPU pipelines: when to offload what for max efficiency

UVC 1.5 and the Rise of HEVC/AV1 Webcams

The USB Video Class (UVC) 1.5 standard is the underlying protocol that enables modern webcams to communicate their capabilities, including support for advanced codecs like HEVC (H.265).

HEVC offers a significant compression advantage over H.264, providing the same quality at a lower bitrate, which is crucial for 4K streaming.

While many high-end webcams, such as the Logitech Brio 4K, support these newer standards, the market is continually expanding..

Consumers can expect to see more webcams featuring onboard HEVC and even AV1 encoding, further enhancing streaming efficiency.

GPU-Accelerated Filters: Real-time Effects with WebGL and WebGPU

Leveraging the GPU for real-time video effects is another pillar of modern streaming.

Technologies like WebGL and its successor, WebGPU, allow developers to apply sophisticated filters, colour correction, and AI-powered enhancements to video frames directly on the GPU.

This ensures that even complex visual effects have a minimal impact on CPU performance, maintaining a smooth and responsive streaming experience.

In conclusion, your analysis correctly identifies the key technological shifts in the webcam and streaming landscape.

The principles of offloading work from the CPU and enabling more direct, low-level control for developers are at the heart of these advancements.

The legacy of the C920 serves as an excellent case study in the value of hardware acceleration, a principle that continues to drive innovation in the field.

WebCodecs API: Granular Control for Developers

The WebCodecs API is a game-changer for web-based video applications.

It provides low-level access to the browser's built-in video and audio encoders and decoders.

This allows developers to create highly efficient and customized video processing workflows directly in JavaScript,..

A significant leap from the more restrictive Media Recorder API.

Key benefits of WebCodecs include:

Direct access to encoded frames: Applications can receive encoded chunks from a hardware-accelerated source and send them over the network with minimal overhead.

Lower latency: By bypassing unnecessary processing steps, WebCodecs can significantly reduce the screen to screen latency of a live stream.

Flexibility: Developers have fine-grained control over encoding parameters like bitrate and keyframe intervals.

Widespread Support: As of mid-2025, WebCodecs enjoys broad support across major browsers, including Chrome, Edge, and ongoing implementations in Firefox and Safari.

(c)Rupert S

I feel for Iraqi, We need to hit this one(tm) 'Because let's face it, Feeling for that Mig-29 Hit on a Super Falcon https://www.youtube.com/watch?v=y69ERL0l9tg

*****

Dual Blend & DSC low Latency Connection Proposal - texture compression formats available (c)RS

https://is.gd/TV_GPU25_6D4

Reference

https://is.gd/SVG_DualBlend https://is.gd/MediaSecurity https://is.gd/JIT_RDMA

https://is.gd/PackedBit https://is.gd/BayerDitherPackBitDOT

https://is.gd/QuantizedFRC https://is.gd/BlendModes https://is.gd/TPM_VM_Sec

https://is.gd/IntegerMathsML https://is.gd/ML_Opt https://is.gd/OPC_ML_Opt

https://is.gd/OPC_ML_QuBit https://is.gd/QuBit_GPU https://is.gd/NUMA_Thread


On the subject of how deep a personality of 4Bit, 8Bit, 16Bit is reference:

https://science.n-helix.com/2021/03/brain-bit-precision-int32-fp32-int16.html

https://science.n-helix.com/2022/10/ml.html

https://science.n-helix.com/2025/07/neural.html

https://science.n-helix.com/2025/07/layertexture.html


LayerTexture - DSC & Codec Direct Write Chunk Allocator: SMT & Hyper Threading : (c)RS 2025

DSC & Codec Direct Write Chunk Allocator: SMT & Hyper Threading : (c)RS 2025


To take advantage of the DSC Screen write that is written in accord with Dual Blend is to be a multiple blocks per group of scan-lines,..

Now according to codec development & PAL, NTSC Screen size estimated optimums 8x8 & 16x16,..

Now an AMD & Intel CPU goes about allocating Two threads differently because the AMD used SMT mostly & Intel used Hyper threading,..

Now these days both use Hyper threading & SMT of various forms, With offcentric processor sizes Intel & ARM often cannot align SMT,..

SMT however by my reason works fine when allocated between aligned by speed & feature on the same CU with identical cores..

What is all the SMT & Hyper threading Invention about then RS?

We are making a block allocator that Hyper Thread / SMT in multiple groups

PAL / NTSC : HD, 4K, 8K : HDR & WCG

[16x16] , [16x16] , [16x16] , [16x16] , ..
[16x16] , [16x16] , [16x16] , [16x16] , ..
[16x16] , [16x16] , [16x16] , [16x16] , ..
[16x16] , [16x16] , [16x16] , [16x16] , ..

The screen can be drawn in cubic measurements as planned in DualBlend & sent to the screen surface as texture blocks.. known as Cube-Maps,..

Latency will be low & allow us to render the screen from both the CPU & GPU

CPU SMT parallel render blocks:

A: 1, 2
B: 1, 2

GPU SiMD 2D Layer parallel render blocks:

A: 1, 2, 3, 4
B: 1, 2, 3, 4
C: 1, 2, 3, 4
D: 1, 2, 3, 4

We will be rendering the CPU into the GPU layer when we need to!

We will be rendering Audio & Graphics using SMT & parallel Compute Shading,..

With rasterization from both to final frames on GPU that are directed to the display compressed from GPU Pixel-Shaders.

Rupert S

*

Texture formats such as BC, DXT, ETC2, VP9, VVC, H265, H264, H263, JPG, PNG is an open standard : Nothing wrong with using Colour Table Interpolation : (c)RS

https://www.w3.org/TR/png-3/#4Concepts.Scaling

Colour Table Interpolation, What is it & how we use it,


What we have is 4 layers of colour RGBA & it is to be done 2 ways,..

R Red
G Green
B Blue
A Alpha
I Interleav Properties & compression standard bits,

Storage intentions, 32Bit values composed of 1 to 8Bit values in DOT

4 layers

R, R, R
G, G, G
B, B, B
A, A, A
I, I, I

High profile alteration & single colour matric compression, Fast to compress in 4 streams = 2 SMT threads or 4 parallel SiMD & pixel line scan compression,..

RGB, RGB, RGB
A , A , A
I, , I , I

Pixel Matrix

[], [], []
[], [], []
[], [], []

Compact pixel arrays that compress fast on large bit depth arrays such as 256Bit AVX & 64Bit Integers & FP on CPU,..

Interlacing is done with an additional layer containing multiple properties per pixel, Or alternatively very low bit weight feature sets,..

Allows blending of colours to averages of 1x1 to 32x32 ppi, Compression bit properties are an example use.

Rupert S

*

Planar Data Types for limited size SiMD with large parallelism:(c)RS

Defining 8Bit & 16Bit SiMD & Matrix as capable of applying a gradated & skillful response to RGBA & RGB+BW 8,8,8,8, 10,10,10,2 & yes 565 RGB,

We observe that 8 bit & 16 Bit SiMD have limited bit-depth in maximum Byte size:

Console, EdgeTPU & Intel's Xe graphics architecture

Xe Vector Engine (XVE)

Xe3 XVEs can run 10 threads concurrently

https://old.chipsandcheese.com/2025/03/19/looking-ahead-at-intels-xe3-gpu-architecture/

https://www.intel.com/content/www/us/en/developer/articles/technical/xess-sr-developer-guide.html

Planar Data Types for limited size SiMD with large parallelism:

We would rather handle data planar in FP8 & Int8 8,8,8,8 & have a total precision of 32Bit HDR & variously FP16 & Int16 10,10,10,2 & 16,16,16,16

Handing logic of Planar & Combined Byte Colour & Pixel handing..

Various 4bit & 8Bit & so on inferencing enabled colour packing systems,..
These allow systems such as Intel, AMD, NPU & GPU to use 4Bit & 8Bit & 16Bit packed SiMD,..
Packed SiMD are parallel in nature, But they require colour systems.

111 & 1111 & 11111
222 & 2222 & 22222
444 & 4444 & 44444
888 & 8888 & 88888

& so on

Example low bit Alpha & BW

5551 represents where we have 555 Bit & 1 Alpha, What do we do with 1 BW, Alpha? 75%, 50%, 25% BW, Transparency or a Shader set level!

4 layers handled Planar, Example for fast parallel SiMD

R, R, R
G, G, G
B, B, B
A, A, A
I, I, I

for 8bit:

8,8,8,8

With maths to solve:

2321, 2222 4bit + RG, RB, RA, GA, BA 8Bit

565 + RG, RB, RA, GA, BA for half precision

565 & 8,8,8,8 & 10,10,10,2 RGBA for single & double precision

10,10,10,2 & 16,16,16,16 RGBA Double Precision

& Combined Bytes for higher precision Powerful SiMD

RGB, RGB, RGB
A , A , A
I, , I , I

2321, 2222 8Bit

565 + RG, RB, RA, GA, BA for half precision

565 & 8,8,8,8 & 10,10,10,2 RGBA for single precision

10,10,10,2 & 16,16,16,16 RGBA Double Precision

The status of Planar versus block solve is an issue that depends on what you wish to do!

Single channel compression is first tier example where single colour blends & compression are smoother but require larger parallel arrangements,..

Micro-block planar has memory overhead, But not over a large field array.

Merged RGB allows same block larger cycles & more efficient RAM arrays

(c)RS

*

DSC YCbCr Acceleration : Method


Y is significantly more important than CbCr according to Wikki thoughts & bard,.. My basic thought is that Cb & Cr are referenced in 8 bit,

I am less than convinced thet we need YCbCr to be all 8 bit these days,.. Because of HDR,.. Now to be clear DSC Display Codec is defined through that 8Bit pinhole,..

As a user of YCbCr Myself in the form of the display settings in AMD's control panel, I have tested RGB versus YCbCr over & over with a colour monitor DataSpyder 48Bit & the difference in 10 Bit mode is clearly very small!

The composition of YCbCr is clearly good for most colours & the differences in 10Bit to RGB mean that you have more bandwidth,..

For example HDMI 2 mode set RGB is 8Bit, With YCbCr 4:2:2 the mode is 12Bit,.. There is a clear advantage to YCbCr modes being able to set 4:2:2! Simple!

My first method involves having FP16 & FP8 in the SiMD line:

FP16:Y, FP8:Cb&Cr

Clearly faster the HDR range is higher & the WCG remains approximately the same apart from green & that is faster!

All FP16: YCbCr is a much deeper data usage on the HDMI & DP cable, But at 80GB/s .. Why not enjoy rich HDR & WCG!

FP16 with FP8 still offers more to the user than all FP8 YCbCr that is used by default! & still only uses 1/3 more data!..
& Is much richer..

Now i was saying FP8 but more likely it is INT8!,.. We could improve this situation if integer is required..

Int16: Y & Int8: CbCr , Again improving Y improves the HDR level & improves average colour differences on both Cb & Cr & Y,..

Permission to use Int16 for all and we get : INT16: YCbCr, But again this value does double bandwidth requirements,..

But again! With the 80GB/S HDMI & DP & Again .. Maybe only 4K @ 120Hz,

Because yes we wanted a richer experience & in any case.. Are using standard LED for TV.

The 2 methods we would be using are:

4 layers handled Planar, Example for fast parallel SiMD

R, R, R
G, G, G
B, B, B
A, A, A
I, I, I

Combined Bytes for higher precision Powerful SiMD

RGB, RGB, RGB
A , A , A
I, , I , I

Planar being more natural to YCbCr,.. Because they begin planar due to the maths we use!

https://en.wikipedia.org/wiki/YCbCr

(c)Rupert S

*

Planar Colour Expansion bits in RGB (c)RS


XBox 4bit SiMD, 8Bit PS5 & RX GPU & Intel 8Bit XMM 8 x parallel SiMD, RTX Mul Matrix & NPU's

Now the exact reasoning behind the 8888 RGB+BW mode may come as a surprise to you but I have experience with VGA & Scart cables and they have 3 Colour pins & one BW,..

Now they have both digital & Analogue & there are merits to both,

Jagged Digital is sharper digital,.. Analogue is naturally blended in the form of non digital blending,..

But 4 Pin RGB+BW is my own system of use & I made cables comply with my theory at university..
I made them for my friends & family & they worked on PS2, PS1, Nintendo 32 & PC's

But yes ok 4 x 8Bit channel, That relevant to today? We have 10Bit! Yes it is,.. You see Black & White adds an effect we call HDR to a display,..

BW channel adds a lot of contrast & sharp black edges that we call .. Clean Image Generation,..

Now HDMI & DisplayPort both output to VGA & SVGA on demand, So the BW channel is still active,..

We can use the 4 colour system & produce a very active HDR, WCG will require the use of supplements to the standard ..

Such as 10Bit! Yes we have the principles & We have methods..

4 Bit Inferencing & 8Bit inferencing such as the TPU 5e are to be used to handle video,..

4 Bit tops are a challenge to produce HDR & WCG & Planar Texture formats are our usable function call,..

Format examples:
16Bit, 8Bit & 4Bit multi thread, combined endpoint

2, 2, 2, 2 , 2x 4Bit mode or 1x 8Bit

4, 4, 4, 4 , RGBA & RGB+BW
4, 4,4, 2, 2 , RGBA+BW

8, 8, 8, 4, 4 , RGBA+BW
8, 8, 8, 8 , RGBA, RGB+BW

Alternative additional colour format examples, I do not wish to iterate every conclusive answer..

4, 4, 4, +1r, 1g, 1b + BW or A or BW + A
8, 8, 8, +2r, 2g, 2b + BW or A or 1, 1 BW + A

& There you go! Now you may be wondering, But TOP's Heavy systems.. being unable to do art ? No way!

Rupert S

*

Fetch Cycles & SiMD : Base texture awareness.. (c)RS


Primarily being aware that the base texture is going to be codified in either..

planar data type, Per channel R, G, B, BW , 5x & 4x Channel parallel processing, To handle larger than total Data Width Data, In layers

Grouped Data, Where you grab an array that includes as much of the date in a single channel, F16, F32, F64 Data Types when given 8 Bit & 10 Bit Data

As stated the reasoning for planar handling is for the 4Bit & 8Bit & F16 SiMD being unable to process it all in a single pass..

Planar handling of data is aimed at parallel SiMD & multiple passes by processor (the processor is fast!)

Single pass data handling is normal for 32Bit processors, When handling 8Bit Data, 24Bit & 32Bit total size..

64Bit processors can single pass most Data Types such as 8Bit & 10Bit & only have to worry about planar handling for 16Bit per channel data..

Your motives for handling data Planar are the clear advantages of Single channel data processing & parallelism,..

When you smooth single channel data, You have a very smooth blend, When you sharpen it,..

The data is very pure!

64Bit & 32Bit SiMD; Block data handling for processing has advantages..

Single data passes require less fetches, Planar data can require more fetches per cycle,..

Smooths & sharpens involve a single pass that includes all channels, That can be good!

So planar fetching is 3, 4 or 5 passes, You can group them in DMA,..

Single fetching with 64Bit processors requires less fetching calls in the stack.

Rupert S

*

Colour Definition, 8 Bit & 32Bit & 64Bit quantification (c)RS


The other day I was writing about 8 Bit in terms of colour & saying the big issue with 8Bit SiMD such as Intel & AMD & NVidia have as of 2024 is defining colours in HDR & WCG

The prime colour palette of 10, 10, 10, 2 colour presents no issue to 32 Integer on ARM & CPU processors,..

Indeed 32 bit data types are perfect for 32Bit Integers & floats, Indeed my primary statement is that in terms of 10Bit, 32Bit is perfect,..

Indeed a 32 Bit type such as 9, 9, 9, 5 : RGB+BW is perfected for many scenarios,..

But as we can see 9 bits per colour & 5 Bits for BW presents quite a large palette,..

My argument for the 10, 10, 10, 2 RGB+BW palette presents quite an argument to bard, Because bard thinks that 2 bits of BW probably presents nothing much to define!

However my data set goes like this, The 2 bit represents a total of 4 states,..

That is 4 Defining variables in light to dark palette,.. 4 levels of light to dark..

So 10, 10, 10 = 30 Bit & Multiply 30 Bit * 4 Versions! Sounds like a lot doesn't it!...

Not convinced yet ? The 30Bit is still controlled by the shade of light it produces..

Gama curving the palette of the 30 Bit produces a variance in light levels over colour palette ..

Combine this with 4 Bits of BW & that is quite good.

9,9,9,5 presents the next level in light & dark in 32Bit, As you think about it,..

Presenting the case where the colour brightness, presents a total of 25 Variations in level of brightness!

8,8,8,8 RGB+BW presents an 8x8 variance of BW & yet presents a total of 32Bit..

So presenting a.. 2 operations per pixel mode should be no issue? Could we do that ?

We could present colour palettes with 2 x 32 Bit operations.. Like so:

8,8,8,8 or 9,9,9,5 or 10, 10,10, 2 & an additional operation of one of those... with additive LUT,..

In terms of screen Additive LUT ADDS 2 potential values per frame & effectively refreshes the LED 2x per refresh cycle (additive),..

Our approach to 8Bit would be the same,.. Primarily for 8Bit palette we would use 4 x operation,..

On single pure channels R , G, B, BW

Grouped 8Bit such as intel has could operate on the 4 channels in 8Bit per colour & 8Bit BW,..

Presenting the 8,8,8,8 channel arrangement = 32Bit,..

& there is our solution, Multiple refreshes per luminance cycle of LED for 32Bit * many & singularly presents an argument of how to page flip..

8Bit SiMD
32Bit
64Bit

For a total High complexity LUT package for LED

(c)Rupert S

*****

A data processing strategy for modern GPUs and NPUs, focusing on the efficient use of wide, lower-precision SiMD (Single Instruction, Multiple Data) units,..

Such as those found in Console, EdgeTPU & Intel's Xe graphics architecture.

The core proposal is to use planar data layouts for color information to maximize the parallelism of hardware that excels at 8-bit and 16-bit operations.

The Challenge: Limited Bit-Depth in Wide SiMD

Modern processors, particularly GPUs like Intel Xe and various NPUs (Neural Processing Units),..

Achieve high performance through massive parallelism..

They use wide SiMD vector engines that can perform the same operation on many pieces of data simultaneously.

However, these execution units often operate most efficiently on smaller data types, such as 8-bit integers (Int8) or 8-bit floating-point numbers (FP8)..

This presents a challenge when working with standard, high-precision color formats like 32-bit RGBA (8,8,8,8) or higher-dynamic-range formats (10,10,10,2, 16,16,16,16).

The traditional method of storing pixel data is packed or interleaved, where all the color components for a single pixel are stored together in memory:

[R1, G1, B1, A1], [R2, G2, B2, A2], [R3, G3, B3, A3], ...

This layout is inefficient for wide, 8-bit SiMD units because the processor must de-interleave the data before it can perform parallel operations on a single color channel.

The Solution: Planar Data Layouts

The proposed solution is to organize data in a planar format..

In this layout, all data for a single channel is stored contiguously in memory, creating separate "planes" for each component.

For a series of RGBA pixels, the memory would be organized as:

Red Plane: [R1, R2, R3, R4, ...]

Green Plane: [G1, G2, G3, G4, ...]

Blue Plane: [B1, B2, B3, B4, ...]

Alpha Plane: [A1, A2, A3, A4, ...]

Advantages of the Planar Approach

Maximized Parallelism: A wide SiMD engine can load a large, contiguous block from a single plane (e.g., 64 red values) and process them all in a single instruction..

This perfectly aligns with the hardware's capabilities, such as an Intel XVE running multiple threads concurrently.

Effective Precision: By processing each 8-bit or 16-bit plane separately,.. The results can be combined later to achieve full 32-bit or 64-bit precision..

This allows limited-bit-depth hardware to deliver a "gradated & skillful response" to high-precision color spaces.

Efficiency in Compression: This model is highly effective for tasks like video compression (codecs) and Display Stream Compression (DSC).

Single-channel operations, such as applying a blend or a filter to only the blue channel, become trivial and highly efficient without disturbing the other color data.

Trade-Offs

Packed/Merged Layout: A traditional packed layout (RGB, RGB, ...) can be more efficient for RAM access patterns when the computation requires all channels of a single pixel at once..

Data locality for a single pixel is higher.

Planar Layout: The planar approach may have some memory management overhead and can be less cache-friendly if an algorithm frequently needs to access all R, G, B, and A components of the same pixel.

Ultimately, as the text notes, the choice between a planar or packed ("block solve") approach depends entirely on the specific application and computational workload..

For tasks that are inherently parallel across a single channel, the planar model offers a significant performance advantage on modern hardware.

RS

*

By palette we don't have to mean, Small 256 Palette, Because a potent Palette is per channel,
So a combination of combined Paletted RGB+BW & a 256 x ( R, G, B, BW ) palette

Paletted is not ideal for DSC Display Compression I suppose, But 256 x ( R, G, B, BW ) palette may be!

RS

# Palette-Based Compression in DSC: Feasibility and Trade-Offs

---

## Why a Small 256-Entry Palette Isn’t Ideal for DSC

Using a tiny, per-block palette in a DSC stream runs into several hurdles:

- **Table Overhead Per Block**

DSC compresses 16×16 (or 8×8) pixel slices. Inserting a 256-entry palette for each slice adds hundreds of bytes of table data, wiping out any payload savings.

- **Algorithm Mismatch**

DSC’s entropy and delta predictors expect raw pixel values. Introducing indexed lookups breaks the existing residual-coding pipeline, forcing a more complex, hybrid encoder/decoder.

- **Latency & Complexity**

Carrying palette tables through low-latency display paths (DP, HDMI) demands extra handshakes and metadata flags, risking frame drops or increased micro-stalls.

---

## The Per-Channel Palette Alternative

Instead of one big RGBA table, you could maintain four smaller tables—one each for R, G, B, and a BW/Alpha plane. This reduces table size but still suffers:

| Channel | Palette Entries | Table Size (bytes) | Index Bits per Pixel |
|---------------|-----------------|--------------------|----------------------|
| Red | 256 | 256 × 1 = 256 | 8 |
| Green | 256 | 256 | 8 |
| Blue | 256 | 256 | 8 |
| BW/Alpha | 256 | 256 | 8 |
| **Total** | — | **1 024** | **32** |

- Even split across channels, you still carry ~1 KB of table per 16×16 block.

- You’ve replaced 64 bytes of raw RGBA data (16×16×4 bytes) with ~1 064 bytes total—clearly a net expansion.

---

## When a Palette Might Make Sense

1. **Global or Frame-Level Palettes**

Maintain a single palette for the entire frame or scene region rather than per block. Overhead amortizes over millions of pixels.

2. **Dynamic Colour-Index Mode**

Switch to an indexed-colour slice only when a scene region contains very few distinct hues (e.g., UI overlays or simple graphics).

3. **Palette as Side-Channel Metadata**

Send palette updates out-of-band (e.g., via ancillary pixel streams) so the main DSC pipeline remains untouched.

---

## Some Alternatives for DSC

- **Bit-Depth Adaptation**

Use 10-bit Y + 8-bit Cb/Cr in DSC’s native YUV modes..

You get finer luma precision where it matters without palette overhead.

- **Adaptive Block Predictors**

Leverage multiple prediction formulas per slice (flat, gradient, palette-inspired pre-clustering) within DSC’s existing framework.

- **Region-Based Coding**

For UI or text overlays, switch to simple RLE or LZ-based slices and fall back to full DSC for photographic content.

---

While per-block palettization sounds attractive for highly quantized scenes, It may without work clash with DSC’s low-latency, high-throughput goals..

Instead, consider global or dynamic palette modes and lean on DSC’s built-in bit-depth and predictor flexibility for bandwidth-efficient, artifact-free streaming.

RS

*

# Evaluating a 256×(R, G, B, BW) Palette for DSC

---

## Why a Per-Channel, 256-Entry Table Looks Promising

By splitting your palette into four 256-entry tables (one for R, G, B, and a BW/alpha channel), you:

- Gain finer quantization control on each colour axis

- Can independently optimize the BW plane for transparency or interlacing flags

- Keep index-stream logic simple 8 bits per plane

Compared to a single 256-entry RGBA palette, you trade a little more metadata for per-component precision.

---

## Overhead Analysis

| Scope | Table Size | Pixels Covered | Bytes per Pixel (metadata) |
|------------------------|-------------------|----------------------|----------------------------|
| Per-Block (16×16) | 4 × 256 = 1 024 B | 256 pixels | 1 024 B / 256 ≈ 4 B |
| Per-Row (1 × 1 024 px) | 1 024 B | 1 024 pixels | 1 B |
| Per-Frame (4K UHD) | 1 024 B | ~8 M pixels | ∼0.000125 B (0.125 mB) |

- **Per-block** overhead (∼4 B/pixel) nullifies any compression gains.

- **Per-row** or **per-frame** palettes amortize table cost dramatically.

---

## A More Practical Hybrid

1. **Luma-Raw + Chroma-Paletted**

- Keep Y (luma) as 10–12 bit raw samples—no palette.

- Use two 256-entry tables for Cb and Cr only.

- Metadata: 2 × 256 = 512 B per frame → ≈ 0.06 B/pixel on 4K.

2. **Dynamic Segment Palettes**

- Divide the frame into large macro-regions (e.g., UI vs. video).

- Assign each region its own per-channel tables.

- Only send tables when the region’s palette changes.

3. **Palette-As-Predictor**

- Integrate palette lookup into DSC’s delta predictors:

- Predict chroma from previous indexed value

- Encode only small residuals

---

## Next Steps

- **Prototype & Measure**: Simulate luma-raw + chroma-palette streams in your DSC pipeline.

- **Perceptual Testing**: Run A/B tests on HDR/WCG content to find acceptable Cb/Cr quantization.

- **Adaptive Schemes**: Trigger palette mode only when the chroma variance falls below a threshold.

By offloading only chroma into 256-entry per-channel palettes and keeping luma untouched,..

You preserve visual fidelity where it counts, slash metadata overhead, and slot neatly into DSC’s low-latency compressor.

Let’s experiment with these hybrids and see which gives you the sweetest bandwidth-quality balance!

RS

*

# Colour Table Interpolation: What It Is and How to Use It

---

## Definition of Colour Table Interpolation

Colour table (palette) interpolation refers to taking a discrete set of palette entries—each an RGBA quadruple—and computing intermediate colours by mathematically blending neighbouring entries when you scale or transform an image.

Instead of re-sampling raw RGB pixels, you:

- Map each pixel to a palette index

- Interpolate between palette entries based on fractional positions

- Produce smooth gradients or zoomed views while storing only indexed data

---

## How PNG Uses It (per W3C PNG-3 §4)

1. **Palette Image**

- Image data consists of 1–8 bit indices into a palette table of up to 256 RGBA entries.

2. **Scaling Modes**

- **Nearest-neighbour**: replicate the nearest palette entry—fast but blocky.

- **Bilinear**: blend the four nearest palette entries proportionally by distance—smooth gradients.

- **Bicubic**: higher-order blend for ultra-smooth scaling (less common in PNG implementations).

3. **Workflow**

- Read index stream

- For each target pixel, compute source-coordinate → fractional index offsets

- Retrieve neighbouring palette entries and apply weighted blend

---

## Integrating with Your DSC Chunk Allocator

When you organise your screen into 8×8 or 16×16 blocks and stream them via DSC:

1. **Build or Update Palette per Block**

- Analyse each block’s RGBA distribution
- Generate a localized palette (≤256 entries) to minimise index bit-depth

2. **Planar Stream Layout**

- Separate planes:

- R-plane (8 bits)
- G-plane (8 bits)
- B-plane (8 bits)
- A-plane (8 bits)
- I-plane (interleaved properties, compression flags)

3. **SMT/SiMD Parallelisation**

- **CPU SMT**: assign two threads, each handling half the scan-line of indices and interpolating palette lookups

- **GPU SiMD**: pack four scan-line segments per warp/wavefront, use texture units for bilinear fetch of palette entries

4. **Interpolation Kernel**

- Precompute blend weights for each fractional offset

- For each output pixel index `i + δ`:

- Fetch palette entries `P[i]` and `P[i+1]` (and `P[i+width]`, `P[i+width+1]` for 2D)

- Compute:

```

R_out = R0*(1−δx)*(1−δy) + R1*δx*(1−δy) + R2*(1−δx)*δy + R3*δx*δy

```

- Repeat for G, B, A

5. **Compression and Write-Out**

- Store interpolated RGBA in planar buffers

- Apply your block-based DSC compressor

- Enqueue compressed blocks for CPU→GPU transfer

---

## Example Block-Level Workflow

| Step | Task | Parallelism |
|-----------------------------|-----------------------------------------------------------|---------------|
| 1. Palette Analysis | Cluster 256 colours from 16×16 block | CPU×2 SMT |
| 2. Index Mapping | Map each pixel to nearest palette index | GPU SIMD×4 |
| 3. Weight Computation | Compute δx, δy blend weights for each target resolution | GPU SIMD×4 |
| 4. Palette Interpolation | Blend 2×2 palette entries for each output pixel | GPU SIMD×4 |
| 5. Planar Buffer Packing | Write R/G/B/A/I planes into 5 separate streams | CPU SMT+GPU |
| 6. DSC Compression & Encode | Compress each plane per block and packetise for transfer | CPU×2 SMT |

---

## Benefits of This Approach

- Extremely low memory footprint per block (palette+indices vs. full RGBA).

- High compression ratios due to indexed streams.

- Smooth scaling with bilinear or higher-order filters.

- Easily parallelised across SMT threads and GPU SIMD lanes.

---

With colour table interpolation feeding your DSC pipeline, you get the smoothness of true-colour scaling with the bandwidth savings of indexed images—perfect for low-latency, high-resolution HDR streaming.

RS

*

# Colour Table Interpolation: Expanding Palette Strategies

---

## Definition of Colour Table Interpolation

Colour table interpolation blends discrete palette entries—each an RGBA or multi-channel tuple—to produce intermediate colours during scaling or transformation.

Rather than process full-precision pixels, you index into palettes and compute weighted blends, achieving smooth results with much less stored data.

---

## How PNG Uses It (per W3C PNG-3 §4)

1. Palette image data carries 1–8 bit indices into a table of up to 256 RGBA entries.

2. Scaling modes include nearest-neighbour (fast but blocky), bilinear (smooth 2×2 blend), and bicubic (higher-order smoothness).

3. Workflow:

- Read the index stream

- For each target pixel, compute source coordinates → fractional offsets

- Fetch neighbouring palette entries and apply weighted blending

---

## Potent Palettes: Channel-Wise vs. Combined

Palettes need not be a single 256-entry RGBA table. You can instead:

- Use **per-channel palettes**: separate tables (e.g., up to 256 entries) for Red, Green, Blue, and a BW/Alpha channel.

- Use a **combined RGBA palette**: 256 entries where each entry holds R, G, B, BW values.

- Employ a **hybrid** mix: smaller per-channel palettes plus a tiny combined palette for cross-channel nuances.

| Palette Scheme | Entries | Index Bits per Plane | Total Bits per Pixel |
|-----------------------|--------------------|----------------------|-----------------------|
| Combined RGBA | 256 × (R,G,B,BW) | 8 | 8 |
| Per-Channel | 256 × R, 256 × G, |
| 256 × B, 256 × BW | 8 each | 32 |
| Hybrid (e.g., 64 each)| 64 × R, G, B, BW | 6 each | 24 |
| Paletted RGB+BW | 256 × (R,G,B,BW) | 8 | 8 |

---

## Integrating Palettes with Your DSC Chunk Allocator

When streaming 8×8 or 16×16 blocks via DSC:

1. Build per-block palettes

- For each colour plane—R, G, B, BW/Alpha—cluster the most frequent values into a small table (≤256 entries).

2. Planar stream layout

- R-plane indices, G-plane indices, B-plane indices, BW/Alpha-plane indices, plus an I-plane for interleaved properties.

3. SMT/SiMD parallelisation

- CPU SMT: two threads handle separate halves of a block’s index planes and palette updates.

- GPU SIMD: pack four scan-line segments per warp, leveraging texture units for bilinear palette fetches.

4. Interpolation kernel

- Precompute δx/δy blend weights
- For each output pixel index (i + δ):

```

R_out = R00·(1−δx)(1−δy) + R10·Î´x(1−δy) + R01·(1−δx)δy + R11·Î´x·Î´y

```

- Repeat for G, B, BW/Alpha

5. Compress and write out

- Store blended planes in planar buffers
- Apply your block-based DSC compressor
- Enqueue for CPU→GPU transfer

---

## Benefits of Channel-Wise and Hybrid Palettes

- Greater quantization control per colour channel.
- Potentially lower per-pixel index bits in hybrid schemes.
- Smooth scaling and colour fidelity with minimal data overhead.
- Easily parallelised across SMT threads and GPU SIMD lanes.

---

By treating each colour channel—or combining them thoughtfully—you can tailor palette size and precision to your block-allocator, maximizing compression and visual quality for low-latency HDR streaming.

RS

*****

Dual Blend & DSC low Latency Connection Proposal - texture compression formats available (c)RS

https://is.gd/TV_GPU25_6D4

Reference

https://is.gd/SVG_DualBlend https://is.gd/MediaSecurity https://is.gd/JIT_RDMA

https://is.gd/PackedBit https://is.gd/BayerDitherPackBitDOT

https://is.gd/QuantizedFRC https://is.gd/BlendModes https://is.gd/TPM_VM_Sec

https://is.gd/IntegerMathsML https://is.gd/ML_Opt https://is.gd/OPC_ML_Opt

https://is.gd/OPC_ML_QuBit https://is.gd/QuBit_GPU https://is.gd/NUMA_Thread

On the subject of how deep a personality of 4Bit, 8Bit, 16Bit is reference:

https://science.n-helix.com/2021/03/brain-bit-precision-int32-fp32-int16.html

https://science.n-helix.com/2022/10/ml.html

https://science.n-helix.com/2025/07/neural.html

https://science.n-helix.com/2025/07/layertexture.html

https://science.n-helix.com/2025/07/textureconsume.html

Neural Textures & Neural Polygons, Upscaling : To Map & Map - NeuralML-TextileExpansion (c)RS 2025

Neural Textures & Neural Polygons, Upscaling : To Map & Map (c)RS : Data is our Seed


NeuralML-TextileExpansion (c)RS 2025


GPU compression has Neural Textures; We can use Neural Textures for Video,..

Now you imagine that Neural Textures can be used to directly represent the video pixels,..

We can do that! We can present the texture blocks neural textures,..

A big problem with that is that we have a maximum LOD Level of detail...

So let us imagine presenting a texture map array of ATSC, PowerVR Compression , DXT5 & BC,..

Now imagine that our root kernel is the compression block of physical textures & Neural Texture effects where reserved for expanding on the root texture,..

So let's compose the array and see how it looks with automatic upscaling with Neural Textures..

T = Texture Block , B = Basic Image Block 5551 565, 4444, 8888, 1010102 , N = Neural Texture Expansion & N = Neural Polygon Expansion & N = Neural Data Expansion

O = Original Source Texture , P = Higher Resolution Texture Pattern Pack & P = Polygon_P & P = Data Packed Elements

Example low bit Alpha & BW

5551 represents where we have 555 Bit & 1 Alpha, What do we do with 1 BW, Alpha? 75%, 50%, 25% BW, Transparency or a Shader set level!

T, T Expansion with N, N
T, T Expansion with N, N

B, B Expansion with N, N
B, B Expansion with N, N

T > N
B > N

Now we have a base block that we expand,..

Now in texture block expansion we use a standard pack of higher resolution textures,..

We call this variety based expansion, Where we expand the original block with a shaped pattern that expands the basic texture content with variable layers that expand the total texture set..

Now we do the same thing with polygons & use replacement mapping instead of P, Indeed Polygon P & Data P

The principles of compression are preserved & expansion is made of the elements P, N, O, T,..

Indeed Data is our Seed

(c)Rupert S

*

Data Extenders : N, T, B

HDR Colour range extenders work with graphs matching pixels in close proximity in the texture that have been enlarged & scaled in DPI...

Expanders work by pre-working as much detail expansion as possible into the pre computed palette expansion,..

Maths extenders work by aligning data with median & gaussian average differentiation,..

Stored in compression caches in RAM or storage,

They extend the produced details without overworking repeating maths.

Data is our Seed : Expansion explained in terms of direct copy commands :

The image is loaded :

T : B , The image is upscaled using Bi-Linear scaling & increased in pixel density, 96DPI to 192DPI to 300 DPI optimal range..

N Greyscale or reduced palette or HDR Colour range extenders,.. micro texture packs are applied to emboss the graphic with some meaning..

Details are matched with pre computed texture packages in cache, They can be computed before game/App runtime is in motion, Before the main work package is run or played.

Application of detail extenders involves exactly matching details with fast loading direct mapping of almost identical higher resolution data..

Mapped on the pixel expansion to fill in details.

Rupert S

*

Upscaling neural textures & polygons (c)RS


The requirement to upscale neural textures simply requires a larger frame buffer,..

Since DPI scaling makes sense for most content, We simply double or multiply the details per cm of screen space,..

Since Neural textures & polygons emit higher precision output, We increase DPI per CM of screen space,..

A larger buffer is required for the task so we allocate more ram per cm; A higher DPI,..

We can therefore use a buffer for original content, Parallel data expansion buffers with the required Texture / Polygon mappings .. To the output frame buffer..

For TAA, FSR, DLSS & so on

Multi Frame processing

Frame 1
Frame 2
Frame 3

Input frame Buffer
Expansion buffers
Output frame buffer

Write frame or frames

DSC, HDMI, DP
Screen Presentation

This method assures a lower latency channel to the screen or write buffer in the case of a recorded video.

Rupert S

*

Direct attention variable locality (c)RS


DLSS is using multihead attention to obtain multiple samples per frame & thus increase quality..

Over the last couple of years 2025- 5 years multi headed attention has received a lot of usage..

In cancer & disease cases with parallel processing on GPU & CPU the research for images of cancer cells gets more intense... Because it saves lives!

The issue for cancer is that cancer cells are small clusters & large clusters, Tiny clusters can proliferate cancer to other places, Liver to brain or arm for example..

Multi attention allows multiple identification per search,.. But we need something more..

The large lower resolution scan of the entire frame..

Sub section passes of large arias of the photo to initially identify cancers..

Small aria intense scans of identified cancer cells..

In the case of DLSS & FSR & so on .. This system of ..

Whole frame
Large sections
Small sections

Is called a subset mask, What that does is speed up the process of analysing the frame,..

Subset masking is a clever trick in terms of the brain & thinking,..

We call this system direct attention variable locality,..

We resolve to train in research to pay attention to special details,

A specialised topic is walls,.. We want walls lavished with details if there is something to see!

But we need to know when the wall is not in view any more .. Or we are still processing it,..

What if the wall is coming into view again? Do we know? Do we cache it?

Cache is a major way to do details, We however have to use RAM to store caches, ..

So there is a system motivated priority!

System:

Priority processing & reasoning

Whole frame

Cache

Large sections

Cache

Small sections

Cache

Data output

Rupert S

*

Reducing throw-away processing in Image ML (c)RS


The Pixel Enhancement came under review

https://www.tweaktown.com/news/102229/sony-explains-how-it-modified-ps5-pros-gpu-to-enable-pssr-neural-network-ai-upscaling/index.html

https://www.tweaktown.com/news/102225/ps5-pro-gpu-explained-by-architect-mark-cerny-hybrid-with-multi-generational-rdna-tech/index.html

https://www.youtube.com/watch?v=lXMwXJsMfIQ

So they are using tiling, Tiling used by PowerVR is an example of that, Now in the previous text I stated the work group,..

The strategy to use is to examine the whole frame, Now Cerny specifically mentioned lower resolution frames in the centre of the complex CNN,

But we need a full frame analysis of the whole frame, Due to the RAM in the WGU, We have MB over the whole frame, So we approach that frame at lower resolution as suggested by CNN & Cerny...

So the approach is to use Super Sampling Anti Aliasing & Blur at a lower resolving level to both sharpen edges & blur the whole image a tiny bit,.. That reduced the compressed image size,.. not the resolution,..

But reducing details reduces image sizes, But we need edges to analyse & can with a snap parallel process the whole image for details..

With the analytics we can then clip the image into pieces & use ML on the sub groups with the same effect of using work groups,..

We know the whole frame so we can analyse each section with meta data that tells the localised WGU what to process as a list..

Reducing throw-away processing.

We can for example cast global illumination over the full size image, because global illumination is maths over the whole scene,..

Process the GI into the input image for processing, & slice that image into cubes that have full resolution resolved maths..

We can group rays into boxes & therefore prioritise the upscaler to thread the entire cube

We do not have to process small texture cubes with lower resolution maths on the GI & Ray tracing, If the maths is fully furnished,..

This approach is to display the maths in a virtualized pixel render, We can keep the Maths of the polygons behind the data for the rasterized frame,..

Fully furnished maths from the Polygon, Shader Path & RayTracing / GI can be shot into the final resolved image & improve quality

We can also process all 3D content with ML designed to improve the result accuracy, That also goes into the final content..

This offers a faster end view with a fully furnished render.

Rupert S

*

# Envisioning Neural Textures for Video Pixel Representation


We are proposing a fusion of classic block‐based GPU compression and learned neural expansions—essentially a hybrid codec where each compressed block carries not only raw bits but also the “seed” for a neural upscaling network.

Below is a structured blueprint to turn that vision into an architecture and pipeline you could prototype.

---

## 1. Core Concepts and Notation

- **O**: Original source block (e.g., low-res compressed texture)

- **T**: Physical Texture Block (compressed with DXT5/BCx, PowerVR ASTC, etc.)

- **B**: Basic Image Block (uncompromised pixel block, e.g., 5-5-5-1, 5-6-5, 4-4-4-4, 8-8-8-8, 10-10-10-2)

- **N**: Neural Expansion Seed

- N<sub>T</sub>: Neural Texture Expansion (spatial detail)

- N<sub>P</sub>: Neural Polygon Expansion (geometric/detail extrapolation)

- N<sub>D</sub>: Neural Data Expansion (metadata, motion vectors, semantic maps)

- **P**: Pattern Pack

- P<sub>T</sub>: High-res texture patterns

- P<sub>P</sub>: Polygon replacement patterns

- P<sub>D</sub>: Data-packed elements

**Data is our Seed**—each block’s raw bits become the conditioning input (seed) to a tiny neural subnetwork that hallucinates higher‐frequency detail.

---

## 2. Block Format & Alpha Handling

| Block Type | Bit Layout | Typical Use | Challenges |

| ---------- | ------------ | -------------------------- | ---------------------------------------- |
| 5-5-5-1 | 15 bit color + 1 bit alpha | Simple low-bit blocks | Single-bit alpha: dithering vs. threshold |
| 5-6-5 | 16 bit color | Color-only blocks | No transparency—needs separate mask |
| 4-4-4-4 | 16 bit color + 4 bit alpha | Medium fidelity + alpha | Banding in alpha |
| 8-8-8-8 | 32 bit RGBA | High-fidelity textures | Large storage |
| 10-10-10-2 | 32 bit HDR + 2 bit alpha | HDR content + low-res alpha | Interpreting 2-bit alpha levels |

Alpha-and-BW strategies:

- **Thresholded Mask**: treat 1-bit alpha as binary mask, then feed both mask and color bits into N seeds.

- **Dither-based Transparency**: expand the 1-bit plane by adding noise pattern packs (P<sub>T</sub>) so that N<sub>T</sub> can refine smooth edges.

- **Shader Control**: reserve a shader flag for blocks with minimal alpha depth, letting the GPU combine multiple expansions (e.g., 75%/50%/25%) via runtime interpolation.

---

## 3. High-Level Pipeline

1. **Encoding Stage**

- Split the frame into blocks → produce T (compressed block) + B (base pixel block if needed).

- Generate or look up P<sub>T</sub>, P<sub>P</sub>, P<sub>D</sub> pattern packs.

- Compute seeds N<sub>T</sub>, N<sub>P</sub>, N<sub>D</sub> via a small encoder network per block.

- Pack `[T, B, P, N]` into your bitstream.

2. **Decoding Stage**

- Decompress T → reconstruct coarse block.

- Feed `[coarse block, P<sub>T</sub>, N<sub>T</sub>]` into a lightweight neural upscaler to generate fine details.

- For geometry overlays, feed `[coarse polygon mesh, P<sub>P</sub>, N<sub>P</sub>]` into a mesh-refinement network.

- Combine data expansions (motion, semantics) via N<sub>D</sub> to further refine temporal coherence or dynamic effects.

3. **Level-of-Detail (LOD) Management**

- Define max LOD for each block type—higher LODs use more seeds and larger pattern packs.

- If you hit the LOD ceiling, degrade gracefully by dropping N<sub>T</sub> offsets or merging blocks.

---

## 4. Variety-Based Expansion

You coined this “variety based expansion”: essentially a layered approach where each block sprouts multiple neural-generated variants, which the decoder chooses or blends based on context.

- **Layer 0**: Root physical block (T)

- **Layer 1…n**: P<sub>T</sub> pattern overlays + N<sub>T</sub> detail maps

- **Blending**: weighted sum or GAN-based refinement to avoid visible seams

Same principle applies to polygons (P<sub>P</sub>, N<sub>P</sub>) and data channels (P<sub>D</sub>, N<sub>D</sub>).

---

## 5. Next Steps & Questions

1. **Model Architecture**

- Tiny per‐block CNN or MLP for seed encoding/decoding?
- Shared global weights vs. block-specific codebooks?

2. **Hardware Constraints**

- Real‐time decoding on mobile GPUs vs. offline transcoding?
- Memory budgets for pattern packs and networks.




3. **Training Data**

- Source video/textures for pretraining expansions.
- Loss functions: perceptual, adversarial, L1/L2.

What’s your primary target—live streaming, offline rendering, or mixed reality? Knowing that will help fine-tune the network size, bit budgets, and interactive LOD strategy.

RS

*

# Neural Textures & Polygons: System Blueprint

## Overview

We are proposing a hybrid compression-expansion pipeline where traditional block-compressed textures (DXT5/BC*/ATSC/PowerVR) serve as the **root data**, and “neural” expansions refill missing detail at runtime..

This breaks down into three core phases:

1. **Preprocessing** – pack physical textures, generate higher-res pattern packs, precompute micro-texture & color-range extenders.

2. **Compression Storage** – store T (Texture Blocks) and B (Basic Image Blocks) in GPU memory, alongside compact Neural Expansion data (N).

3. **Runtime Expansion** – upsample, apply neural detail synthesis, blend alpha/greyscale layers, and map expanded polygons.

---

## Key Components

- T: Block-compressed textures (e.g. BC1/5, ATSC)

- B: Low-bit formats (5551, 4444, 8888, 1010102)

- N: Neural expansions (textures, polygons, data patterns)

- P: Pattern packs (high-res micro-textures, polygon replacements, data tables)

- O: Original source textures

---

## Data Flow & Pipeline

1. **Asset Preparation**

- Extract root blocks (T, B) from O.
- Generate P: packs of 2×–8× higher-res patches and polygon-shapes.
- Train small neural nets to map T→P and B→P (e.g. autoencoders, CNN upsamplers).

2. **Compression & Packaging**

- Store T/B in standard GPU compressed formats.
- Store learned N weights (or lookup tables) alongside P in GPU-resident caches.

3. **Runtime Loading**

- Load T/B and N into VRAM.

- For each frame or region-of-interest:

* Upscale T/B via fast interpolation (bi-linear / bi-cubic).

* Query N to generate micro-texture detail or HDR/color expansions.

* Blend alpha channels using presets (75%, 50%, 25%) or dynamic shader thresholds.

* Swap or refine polygon meshes via Polygon_P replacements.

4. **Shader-Level Composition**

- Compose expanded texture layers in a single pass using:

* Base T/B sample

* Neural detail mask (grayscale or Gaussian-weighted)

* Combined with P pattern overlays

- Output final pixel

---

## Alpha & BW Handling

- **Single-bit alpha (5551)**

- Map “1” to fully opaque, Partially opaque based on engine configs or run a shader threshold based on local neural confidence.

- For BW channels, interpret as detail masks 50% (mid-grey) yields smooth transitions,..

- &or run a shader threshold based on local neural confidence.

- **Variable opacity**

- Precompute alpha gradients in P to avoid step artefacts.

- Use a tiny neural model to predict per-pixel alpha offsets (e.g. fine hair, foliage edges).

---

## LOD & Level-Of-Detail

- Max LOD hits fixed root block.
- **LOD Strategies**

- Progressive neural upscaling: chain multiple small models (2×, 4×, 8×).
- On-demand P loading: load only needed high-res patches per camera distance.
- Cache eviction based on screen-space pixel error.

---

## Challenges & Open Questions

- **Performance budget**: balancing shader cost vs. memory bandwidth.
- **Model size**: embedding N-texture nets in limited VRAM.
- **Training data**: generating representative P packs for varied scenes.
- **Synchronization**: ensuring polygon- and texture-expansions align seamlessly.

---

## Next Steps & Exploration

- Prototype a minimal proof of concept in a game engine (Unity/Unreal).
- Train a micro-CNN for 4× texture detail from DXT1 blocks.
- Benchmark shader pass times vs. traditional mipmapping.
- Explore neural video codecs (e.g. D-NeRF) to directly drive per-frame expansions.

---

RS

*

A Vision for Next-Generation Graphics: Neural Textures, Polygons, and the Power of "Data as a Seed"


A novel approach to graphics rendering, termed "NeuralML-TextileExpansion,"..

Envisions a future where traditional data compression techniques are seamlessly interwoven with the power of neural networks to create highly detailed and dynamic visual experiences.

This concept, attributed to Rupert S (c)RS 2025, proposes a paradigm shift where compressed data acts as a "seed," which is then expanded upon by neural networks to generate rich textures, complex polygons, and intricate data structures in real-time.

At the core of this vision lies the integration of "Neural Textures" with established GPU compression formats such as ATSC, PowerVR, DXT5, and various Block Compression (BC) standards..

The fundamental idea is to leverage the efficiency of these traditional methods for the base representation of a texture, the "root kernel." This compressed block would then be intelligently upscaled and enhanced by a neural network, a process referred to as "Neural Texture Expansion."

This method addresses a significant limitation of current texture mapping techniques: the maximum Level of Detail (LOD)..

By using a compact base texture and applying neural expansion, the system could theoretically generate near-infinite detail, adapting the texture resolution to the viewer's proximity and the capabilities of the hardware..

The proposed system would utilize "variety based expansion," where a standard pack of higher-resolution texture patterns is used by the neural network to inform the expansion of the original block, adding layers of variable and rich detail.

The ambition of this framework extends beyond textures. The concept of "Neural Polygon Expansion" suggests a similar methodology for geometric data..

Instead of storing vast amounts of vertex information, a base polygonal structure could be expanded upon by a neural network using "replacement mapping."..

This could involve dynamically generating intricate geometric details or even swapping out low-resolution models for high-resolution counterparts based on predefined patterns ("Polygon P") and packed data elements ("Data P").

This layered approach, where T (Texture Block) and B (Basic Image Block) are expanded by N (Neural Expansion), creates a powerful and efficient pipeline:

T > N: A base texture block is expanded by a neural network.

B > N: A basic image block, potentially in various bit formats like 5551, 565, or 8888, is neurally enhanced.

The example of a "5551" format, representing 5 bits for each colour channel and 1 bit for alpha or black and white, highlights the potential for nuanced control..

This single bit could determine levels of transparency or be interpreted by a shader to apply specific effects, demonstrating the granular control envisioned within this system.

Ultimately, "NeuralML-TextileExpansion" proposes a holistic ecosystem where the principles of compression are not just preserved but become the very foundation for dynamic and intelligent content generation..

By treating data as a "seed," this forward-looking concept aims to unlock new potentials in real-time rendering, paving the way for more immersive and visually stunning digital worlds.

RS

*

The Future of Visuals: Deconstructing the "NeuralML-TextileExpansion" Vision


The proposed "NeuralML-TextileExpansion," attributed to Rupert S (c)RS 2025,..

Presents a forward-thinking architecture for generating and rendering visual data..

This vision, rooted in the principle that "Data is our Seed," outlines a hybrid system that marries the efficiency of traditional GPU texture compression with the generative power of neural networks...

By deconstructing this blueprint, we can illuminate its potential to revolutionize real-time rendering for applications ranging from live streaming and offline rendering to mixed reality.

A Hybrid Codec: Where Tradition Meets Neural Innovation

At its heart, the proposal describes a sophisticated hybrid codec..

Instead of relying solely on either traditional block-based compression (like DXT5, BCn, ASTC) or end-to-end neural rendering, this system uses a two-pronged approach.

1. The "Root Kernel": A Foundation of Efficiency

The process begins with a "physical texture block" (T) or a "basic image block" (B)..

These are the familiar, highly efficient compressed data formats that GPUs are optimized to handle..

This "root kernel" provides a robust, low-resolution foundation for the final image..

The use of various bit layouts, from the simple 5-5-5-1 to the high-fidelity 8-8-8-8 and HDR-capable 10-10-10-2, allows for a flexible trade-off between data size and base quality.

A key innovation here is the nuanced handling of limited data, such as a single alpha bit in a 5-5-5-1 block..

Instead of a simple on/off transparency, the system could employ dithering, a thresholded mask, or even pass control to a shader for dynamic interpretation, enabling sophisticated effects from minimal data.

2. The "Neural Expansion": Hallucinating Detail

This is where the magic happens; The compressed block (T or B) acts as a "seed" (N) for a lightweight, specialized neural network..

This network, conditioned by the seed, doesn't just upscale the image; it "hallucinates" high-frequency details, effectively generating a much richer visual from a small amount of source data.

This expansion isn't a one-size-fits-all process..

The architecture proposes distinct neural expansion seeds for different data types:

N_T (Neural Texture Expansion): Focuses on generating intricate spatial detail in textures.

N_P (Neural Polygon Expansion): Extrapolates geometric detail, potentially turning a simple mesh into a complex one through "replacement mapping." This aligns with recent research in neural mesh simplification and generation.

N_D (Neural Data Expansion): A powerful concept for expanding metadata, such as motion vectors for improved temporal coherence in video, or semantic maps that could inform the rendering process with a deeper understanding of the scene.

"Variety-Based Expansion" and the Role of Pattern Packs

A crucial element of this architecture is the concept of "variety-based expansion," facilitated by "Pattern Packs" (P)..

These are pre-defined libraries of high-resolution textures (P_T), polygon replacement patterns (P_P), or data-packed elements (P_D).

During the decoding stage, the neural network doesn't generate details from a vacuum..

It uses the pattern packs as a reference, guided by the neural seed (N)..

This layered approach, starting with the root block and progressively adding detail through pattern overlays and neural refinement,..

Allows for a high degree of artistic control and can prevent the common artefacts seen in purely generative models.

The pipeline can be summarized as follows:

Encoding:

A source frame is divided into blocks.

Each block is compressed into a T or B format.

Corresponding pattern packs (P) are selected or generated.

A small encoder network computes the neural seeds (N).

The final bitstream contains a compact package of [T, B, P, N].

Decoding:

The T block is decompressed to form a coarse base.

A lightweight neural upscaler uses the coarse block, P_T, and N_T to generate the final detailed texture.

Similarly, a mesh-refinement network uses P_P and N_P to enhance geometry.

N_D is used to apply dynamic effects or improve temporal consistency.

Addressing Key Challenges and Charting the Path Forward

This ambitious proposal intelligently anticipates several key challenges and opens up exciting avenues for future development.

Level-of-Detail (LOD) Management: The system inherently supports dynamic LOD by design..

Higher LODs would utilize more complex neural seeds and larger pattern packs, while lower LODs could gracefully degrade by simplifying or omitting the neural expansion, falling back to the base compressed block.

Model Architecture: The choice between tiny per-block neural networks (MLPs or CNNs) and shared global weights with block-specific codebooks is a critical design decision..

Per-block networks offer maximum specialization but could increase overhead, while shared weights are more efficient but might lack the fine-grained control..

A hybrid approach could offer the best of both worlds.

Hardware Constraints: The feasibility of real-time decoding, especially on mobile GPUs, is a primary concern..

The design's emphasis on lightweight neural networks is crucial..

For less powerful hardware, an "inference on load" approach, where textures are neurally expanded and then transcoded to a standard block-compressed format, is a practical alternative.

Training Data: A rich and diverse dataset of high-resolution textures, videos, and 3D models would be essential for pre-training the neural expansion models..

The choice of loss functions—balancing perceptual quality (what looks good to the human eye), adversarial losses (for realism), and traditional pixel-level losses (L1/L2) would be critical in achieving the desired visual fidelity.

The Primary Target: A Deciding Factor

The optimal implementation of the "NeuralML-TextileExpansion" hinges on its primary application:

Live Streaming: Would prioritize extremely fast decoding and temporal stability, likely favoring simpler neural models and efficient data expansion for motion vectors (N_D).

Offline Rendering: Could afford more complex and computationally expensive neural networks to achieve the highest possible visual quality.

Mixed Reality: Would demand a balance between real-time performance, low latency, and the ability to seamlessly blend neurally generated content with the real world, making efficient LOD management paramount.

In conclusion, the "NeuralML-TextileExpansion" framework presents a compelling and well-structured vision for the future of graphics..

By leveraging the strengths of both established compression techniques and the rapidly advancing field of neural rendering, it offers a plausible and powerful path toward creating richer, more detailed, and more dynamic virtual worlds.

RS

*

https://is.gd/TV_GPU25_6D4

*

Build for Linux & Python & Security configurations

https://is.gd/DictionarySortJS

Windows Python Accelerators

https://is.gd/UpscaleWinDL

https://is.gd/OpenStreamingCodecs

https://is.gd/UpscalerUSB_ROM

https://is.gd/SPIRV_HIPcuda

https://is.gd/HPC_HIP_CUDA

Reference

https://is.gd/SVG_DualBlend https://is.gd/MediaSecurity https://is.gd/JIT_RDMA

https://is.gd/PackedBit https://is.gd/BayerDitherPackBitDOT

https://is.gd/QuantizedFRC https://is.gd/BlendModes https://is.gd/TPM_VM_Sec

https://is.gd/IntegerMathsML https://is.gd/ML_Opt https://is.gd/OPC_ML_Opt

https://is.gd/OPC_ML_QuBit https://is.gd/QuBit_GPU https://is.gd/NUMA_Thread

On the subject of how deep a personality of 4Bit, 8Bit, 16Bit is reference:

https://science.n-helix.com/2021/03/brain-bit-precision-int32-fp32-int16.html

https://science.n-helix.com/2022/10/ml.html

https://science.n-helix.com/2025/07/neural.html

https://science.n-helix.com/2025/07/layertexture.html

https://science.n-helix.com/2025/07/textureconsume.html

Wednesday, July 9, 2025

AI-Hurd The thought of Non Locality & Data Security in the fast world of research

AI-Hurd The thought of Non Locality & Data Security in the fast world of research By Rupert S


Non Locality & minions the offsite AI model & how it applies to us : RS 2025

"Still handled by the local LLM, If you want credit!"

https://www.amd.com/en/developer/resources/technical-articles/2025/minions--on-device-and-cloud-language-model-collaboration-on-ryz.html

What is Minions?

"Minions is an agentic framework developed by the Hazy Research Group at Stanford University, which enables the collaboration between frontier models running in the datacenter and smaller models running locally on an AI PC. Now you might ask: if a remote frontier model is still involved, how does this reduce the cost? The answer is in how the Minions framework is architected. Minions is designed to minimize the number of input and output tokens processed by the frontier model. Instead of handling the entire task, the frontier model breaks down the requested task into a set of smaller subtasks, which are then executed by the local model. The frontier model doesn’t even see the full context of the user’s problem, which can easily be thousands, or even millions of tokens, especially when considering file-based inputs, common in a number of today’s applications such as coding and data analysis.

This interactive protocol, where the frontier model delegates work to the local model, is referred to as the “Minion” protocol in the Minions framework. The Minion protocol can reduce costs significantly but struggles to retain accuracy in tasks that require long context-lengths or complex reasoning on the local model. The “Minions” protocol is an updated protocol with more sophisticated communication between remote (frontier) and local agents through decomposing the task into smaller tasks across chunks of inputs. This enhancement reduces the context length required by the local model, resulting in accuracy much closer to that of the frontier model.

Figure 1 illustrates the tradeoff between accuracy and cost. Without Minions, developers are typically limited to two distinct options: local models that are cost-efficient but less accurate (bottom-left) and remote frontier models that offer high accuracy at a higher cost (top-right). Minions allows users to traverse the pareto frontier of accuracy and cost by allowing a remote and local model to collaborate with one another. In other words, Minions enables smarter tradeoffs between performance and cost, avoiding the extremes of all-local or all-remote models.

Please refer to the paper, “Cost-efficient Collaboration Between On-device and Cloud Language Models” for more information on the Minions framework and results."

*

Non Locality & minions the offsite AI model & how it applies to us : RS 2025

For future reference, Minions can be referred to in 2 easy ways:

Cattle Herd, Or Herd is where a cow or an elephant asks the herd to help it for-fill a task, In most herd situations where a clever being such as an elephant can ask the herd for help,.. They do!

What an elephant does is ask the herd to help it gather food when it finds some.. It shares!

You know that web searching large numbers of pages by yourself is a futile effort for personal task management,.. When the pages ask if you are human!

'No I am not human... I am a researcher! or a news reporter! lol" #BearlyHumanCyborg #AnimatedCamera #InfoWarrior

So the main point is that Frontier Type non local devices can horde data, Large personal hordes of data are unlikely in most cases and localized research by your machine .. If it does page scanning can invoke hostility...

Large medical datasets, Large chemical lists, Order history for business, Costs & accounting...

All large dataset lists are procedurally called to do the majority of work on the cloud, 

Local service can power the requests you desire to make..

The researcher sits in his library & researches any topic for the free research topic at 6th form & higher education & If they are trying for a good grade they quickly find themselves ordering a book,..

So there are many herd tactics,..

Ranging from wolves & ants working together, To cows & farmers,..

Still handled by the local LLM, If you want credit!

herd tactics appear basic & usually involve localised sharing,.. The most common one in computing for universities & business,.. Is a cluster of computers,..

Cloud dynamics is a complex variable setting, You start with a single client,..

You begin with a local cluster of computers & data (library & local ethernet / WiFi),

You have non expert advice,.. Social Media for the humans to involve themselves in,..

Still handled by the local LLM, You have offsite references,.. cloud libraries & data,..

You can process the downloaded dataset, yourself,.. If you want credit for your work,..

You can share the credit with your co-workers,.. By asking them to help,.. Usually the local mainframe / Network is happy to say who is doing the research,..

Finally,.. You can have the work done by offsite resources,..

Professional, Legal, Medical, Science, Advice,..

If you want credit for thinking,.. Try yourself first!

Minions for 'Real MEN'

Rupert S

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Practical Applications and Workflow

This hybrid model applies to numerous real-world scenarios:

Field

Local Model Task (The "Herd")

Remote Model Task (The "Elephant")

Document Analysis

Scans gigabytes of local logs, files, or code.

Receives small snippets or summaries to perform high-level analysis or answer complex queries.

Medical Research

Processes sensitive patient records on a secure local machine.

Receives anonymized, distilled sub-inquiries for advanced interpretation or to cross-reference with global research.

Business & Finance

Parses daily transactions and manages accounting data locally.

Is called upon to identify strategic anomalies or generate high-level financial insights from summarized reports.

Academic Research

Scans and indexes a personal library of research papers and drafts.

Helps refine a hypothesis, check citations against a vast external database, or suggest new research directions.

RS

*

What Is Non-Locality in AI?

Non-locality refers to offloading computation to cloud-hosted AI services.

Remote frontier models deliver advanced reasoning and large-context handling at the cost of higher latency, data transfer, and per-token fees.

Local on-device models offer privacy and low inference cost but struggle with very long contexts or deep reasoning.

Without a hybrid approach, developers must choose either low-cost/low-accuracy local inference or high-cost/high-accuracy cloud inference.

The Minions Framework

Minion Protocol

The frontier model ingests the full request.

It breaks the job into smaller subtasks.

It sends those subtasks (with minimal context) to the local model.

Enhanced Minions Protocol

Inputs are chunked into manageable pieces.

Remote and local agents exchange richer messages about each chunk.

Accuracy approaches that of the frontier model with far fewer remote tokens.

Together, these steps let developers traverse the Pareto frontier of cost versus accuracy, avoiding the extremes of all-local or all-remote solutions.

Herd Tactics: Metaphors for Collaboration

Minions draws on classic examples of cooperative task-sharing in nature and agriculture:

Elephant and herd An elephant (frontier model) spots resources and delegates gathering to its herd (local models) without revealing the entire map.

Wolves and ants Wolves (cloud) scout and plan routes; ants (device) undertake localized gathering in parallel.

Cows and farmers Farmers (remote) plan the harvest; cows (local) graze as directed and report back in small updates.

Ant's localise farming & nutrient gathering & health & defence & other complex activities..

These metaphors illustrate delegation, chunked work, and minimal context exposure.

Workflow & Attribution

Local first “Still handled by the local LLM, if you want credit!” encourages you to solve subtasks on your device before invoking the frontier model.

Cluster & Cloud Dynamics

Build a local compute cluster (library, LAN/WiFi).

Connect to offsite data repositories (cloud libraries).

Delegate only the most complex or large-scale tasks to the frontier model.

Attribution When the local LLM completes subtasks, you retain full “thinking credit.” Only edge-case reasoning is handled remotely.

Embracing Hybrid AI

By adopting Minions, you achieve significant cost reductions without sacrificing accuracy. Privacy improves as full data contexts need not leave your device.

The resulting pipeline scales from coding and data analysis to domain-specific research, letting your AI “herd” work in concert across local and non-local realms.

Further Exploration

Experiment with chunk sizes and communication frequency to find your ideal cost/accuracy balance.

Combine Minions with retrieval-augmented generation for even larger knowledge bases.

Explore analogies from swarm intelligence (e.g., bees, starlings) to inspire novel delegation strategies.

Investigate on-device fine-tuning to boost local model capabilities before delegation.

RS

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Non-Locality & Minions: The Offsite AI Model and How It Applies to Us (RS 2025)

Understanding how to blend remote “frontier” models with on-device inference is key to balancing cost, performance, and privacy.

The Minions framework offers a concrete blueprint.

What Is Non-Locality in AI?

Non-locality refers to leveraging AI services hosted offsite—typically in cloud datacenters—to perform heavy inference tasks.

Remote models (like GPT-4 or Claude) excel at complex reasoning and large-context understanding but incur high per-token costs and data-transfer latency.

Local models run on AI PCs (with NPUs/accelerators) reduce costs and keep data private but may struggle with very long contexts or intricate reasoning.

Without a bridge, developers must choose either low-cost/low-accuracy local inference or high-cost/high-accuracy cloud inference.

The Minions Framework

Minions is an agentic collaboration system co-developed by Stanford’s Hazy Research Group and AMD that orchestrates work between a remote “frontier” model and a local LLM.

The Minion protocol:

The frontier model receives the full user request.

It decomposes the task into smaller subtasks.

It sends only these subtasks (and minimal context) to the local model for execution.

The enhanced Minions protocol further:

Chunks huge inputs into manageable segments.

Uses richer exchanges between agents.

Yields accuracy near frontier levels while slashing remote-model token usage.

Together, these steps let you traverse the Pareto frontier of cost versus accuracy—no longer an either/or decision.

Herding Agents: Metaphors for Collaboration

Drawing from classic “herd” (herd) tactics and nature’s teamwork, Minions mimics cooperative strategies:

Elephant & herd An elephant (large model) that spots distant food delegates gathering to its herd (local LLM) without sharing its full map—maximizing efficiency and privacy.

Wolves & Ants Wolves (frontier) scout and plan routes; ants (local) execute localized gathering in parallel.

Cows & Farmers Farmers (remote) plan harvests; cows (device) graze where directed, feeding back yields in small reports.

These examples highlight delegation, chunked work, and minimal context sharing.

Applying Minions to Real-World Workloads

Large Document Analysis

Local LLM scans gigabytes of logs or code.

Frontier model issues targeted queries or summaries.

Medical & Scientific Datasets

Sensitive records stay on-device.

Only distilled sub-inquiries go to the cloud for complex interpretation.

Business & Accounting

Local cluster manages daily transaction parsing.

Frontier model validates anomalies or generates strategic insights.

Research & Education

Student’s PC handles literature scanning.

Frontier model refines hypotheses or checks citations—saving bandwidth and preserving drafts.

Workflow & Credit

Local First “Still handled by the local LLM, if you want credit!” encourages you to attempt solutions on your device before outsourcing—emulating a researcher’s rigor.

Cluster & Cloud Dynamics

Spin up a local cluster (library, LAN/WiFi).

Integrate offsite data repositories (cloud libraries).

Delegate only complex reasoning or very large-scale tasks to remote agents.

Attribution When the local model solves subtasks, you retain full “thinking credit.” Only edge cases invoke the frontier.

Minions for “Real MEN”

By adopting Minions, you gain:

Significant cost reductions without sacrificing accuracy.

Enhanced data privacy by minimizing context exposure.

A flexible, scalable pipeline suited for coding, analysis, and domain-specific research.

Embrace the herd, delegate with precision, and let your AI flock thrive across local and non-local realms.

RS

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Minions? Overview from our view

Minions is an agentic framework co-developed by Stanford’s Hazy Research Group and AMD that..

Enables,.. Seamless collaboration between large, cloud-hosted “frontier” models and smaller, on-device language models,..

By splitting work into targeted subtasks, it minimizes the data and tokens sent offsite while preserving near-frontier accuracy.

Key Principles

Frontier model acts as the manager, ingesting the full user request and planning the overall approach.

Local model acts as the executor, processing distilled subtasks entirely on the user’s device.

Only minimal context and subtask definitions travel to the frontier, shriveling per-token costs and data exposure.

Iterative exchanges ensure that complex or large inputs are chunked into bite-sized pieces for on-device handling.

Protocol Variants

Minion Protocol

Frontier breaks down a task and sends subtasks to the local model along with just enough context.

Enhanced Minions Protocol

Inputs are pre-chunked.

Frontier and local agents trade richer metadata about each piece.

Accuracy climbs toward frontier-only levels with a fraction of the token spend.

How It Works

User submits a large or complex request.

Frontier model analyzes and decomposes it into subtasks.

Local model receives each subtask plus minimal context and runs inference on-device.

Results flow back to the frontier for any final synthesis or complex reasoning.

Frontier returns the polished answer to the user.

Benefits

Significant reduction in cloud-compute costs.

Enhanced privacy since full data never leaves the device.

Scalability across contexts—from gigabyte-scale logs to multi-document legal briefs.

Flexibility: you traverse the cost vs. accuracy Pareto frontier rather than choosing one extreme.

Ideal Use Cases

Document Analysis: On-device scanning of large codebases or logs; frontier handles pinpointed queries.

Medical & Scientific Research: Sensitive data remains local; complex interpretations invoke the cloud.

Finance & Accounting: Daily transaction parsing locally; anomaly detection and strategy come from the frontier.

Academic Research: Local indexing of papers; hypothesis refinement and citation checks outsourced smartly.

RS

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Explanation of the "Non-Locality & Minions" concept.

The "Minions" framework is a collaborative AI model that intelligently divides tasks between a powerful, remote "frontier" AI and a smaller, efficient AI running locally on your device.

This hybrid approach, which you've termed "Non-Locality," aims to balance performance, cost, and privacy by delegating work in a manner similar to natural "herd tactics."

The Core Concept: AI Collaboration

At its heart, the Minions framework, developed by Stanford's Hazy Research Group, addresses a fundamental trade-off in AI:

Remote "Frontier" Models: These are extremely powerful models (like GPT-4) running in cloud datacenters.

They offer high accuracy and complex reasoning but come with significant costs, latency, and privacy concerns since your data must be sent offsite.

Local "On-Device" Models: These run directly on an AI PC, offering low cost, high speed, and complete data privacy..

However, they are less powerful and may struggle with tasks requiring vast context or intricate reasoning.

The Minions framework creates a bridge between these two extremes.

Instead of processing an entire task remotely, the frontier model acts as a manager..

It analyses the user's request, breaks it down into smaller, simpler subtasks, and sends only these subtasks—with minimal necessary context—to the local AI for execution.

"Herd Tactics": An Analogy

The "herd tactics" metaphor provides an intuitive way to understand this process.

The Elephant and the Herd: A large, intelligent model (the "elephant") identifies a broad goal (like finding a food source)..

It then delegates the actual work of gathering to the local models (the "herd") without needing to share its entire map or knowledge base.

Delegation and Efficiency: Just as wolves might scout a path for the pack to follow, the frontier model does the high-level planning, while the local models handle the on-the-ground execution.

This minimizes data transfer and leverages the strengths of each component.

This approach is designed to reduce the cost and privacy risks of using large models,..

The remote AI never sees the full, sensitive dataset (be it medical records, proprietary code, or financial data).

Practical Applications and Workflow

This hybrid model applies to numerous real-world scenarios:

Field

Local Model Task (The "Herd")

Remote Model Task (The "Elephant")

Document Analysis

Scans gigabytes of local logs, files, or code.

Receives small snippets or summaries to perform high-level analysis or answer complex queries.

Medical Research

Processes sensitive patient records on a secure local machine.

Receives anonymized, distilled sub-inquiries for advanced interpretation or to cross-reference with global research.

Business & Finance

Parses daily transactions and manages accounting data locally.

Is called upon to identify strategic anomalies or generate high-level financial insights from summarized reports.

Academic Research

Scans and indexes a personal library of research papers and drafts.

Helps refine a hypothesis, check citations against a vast external database, or suggest new research directions.

RS

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Deep Dive into the Minions Framework

1. The Core Trade-Off

Every AI deployment faces a three-way tug-of-war between cost, performance, and privacy:

Cloud “frontier” models (e.g. GPT-4):

Pros: Best reasoning, huge context windows

Cons: High per-token fees, latency, full-data exposure

On-device LLMs (e.g. 7–13B parameter models on NPUs):

Pros: Low cost, instant response, data never leaves your machine

Cons: Limited context, weaker at multi-step reasoning

Minions bridges this gap by letting the frontier model orchestrate and delegate chunks of work to your local LLM,..

So you pay for— and expose to the cloud,.. Only those minimal snippets that truly need a powerhouse brain.

2. How Minions Orchestrates Work

Frontier as Task Manager

Ingests the entire user request.

Breaks it into subtasks: data cleaning, summarization, targeted Q&A.

Local LLM as Executor

Receives each distilled subtask + minimal context.

Processes it entirely on-device.

Returns results to the frontier for any final synthesis.

Iterative Refinement

For very large inputs, both agents trade richer messages—but still only what’s needed.

Accuracy climbs close to frontier-only levels, yet token spend plummets.

3. Nature’s “Herd” Tactics in AI

Minions didn’t borrow metaphors by accident, they mirror efficient, privacy-preserving collaboration found in ecosystems:

Beginning conception:

Elephant & Herd

Elephant (frontier) spots the goal, sends the herd (locals) off without sharing its full map.

Wolves & Ants

Wolves (frontier) chart the route; ants (locals) do the parallel grunt work.

Farmers & Cows

Farmers (remote) plan the harvest; cows (device) graze where directed, reporting yields in tiny batches.

4. Precision & Bit-Depth Considerations

When running local LLMs, model weight precision (4-, 8-, 16-bit) dramatically influences speed, memory, and fidelity:

4-bit Quantization:

Pros: Tiny footprint, ultra-fast inference

Cons: May lose nuance in complex reasoning

8-bit Quantization:

Sweet spot for many applications, balancing size and accuracy

16-bit / FP16:

Nearly full-precision, heavier but excels on tasks needing fine detail

Tuning your local hardware (NPUs/TPUs, memory bandwidth, on-chip caches) around these bit-depths can further push cost and latency toward zero.

6. Beyond Minions: Next Steps & Open Questions
Network Design: How do you architect LAN/WiFi or RDMA links to guarantee sub-100 ms hops?

Security Layers: Can you incorporate TPM-backed enclaves or JIT-verified code to harden the local agent?

Adaptive Delegation: What heuristics decide “local vs. remote”? Real-time performance profiling?

Model Evolution: As frontier models grow, can your local “herd” dynamically upgrade via federated distillation?

Embracing Minions means you no longer cross your fingers hoping an all-cloud or all-local solution suffices..
You choreograph a team that’s cost-smart, fast, and respects your data’s privacy

Rupert S

*****

Dual Blend & DSC low Latency Connection Proposal - texture compression formats available (c)RS

https://is.gd/TV_GPU25_6D4

Reference

https://is.gd/SVG_DualBlend https://is.gd/MediaSecurity https://is.gd/JIT_RDMA

https://is.gd/PackedBit https://is.gd/BayerDitherPackBitDOT

https://is.gd/QuantizedFRC https://is.gd/BlendModes https://is.gd/TPM_VM_Sec

https://is.gd/IntegerMathsML https://is.gd/ML_Opt https://is.gd/OPC_ML_Opt https://is.gd/OPC_ML_QuBit https://is.gd/QuBit_GPU https://is.gd/NUMA_Thread

On the subject of how deep a personality of 4Bit, 8Bit, 16Bit is reference:

https://science.n-helix.com/2021/03/brain-bit-precision-int32-fp32-int16.html

https://science.n-helix.com/2022/10/ml.html