Firmware - Layered PCB + Dynamic Memory Access (c)RS

Layered PCB + Dynamic Memory Access (c)RS

Demonstrating Memory Access from, direct Layered PCB Between GPU + Storage & RAM,

First requirement of the PCB is to have channels between the GPU & RAM..

Usually these channels need to be at the top of the PCIe & use Channel 2 on the PCIe & DRAM Cycle as a priority..

Direct Access Channels are a special array on the Motherboard that directly allows access to independent PCIe & RAM access channels,

They do not burden the CPU, Although in order to directly interface with the CPU, We need channels to CPU, GPU, Storage & RAM..

The reason to allocate Cycle 2 Dynamically & Only use Cycle 1 when flooded with requests..

Is so that the Main CPU & GPU & RAM component thread can access peak bandwidth first & this reduces latency..

As you may be aware most RAM is 2 Cycles & Most PCIe is 2 to 4 Cycles..

The Firmware Bios of the motherboard chipset has 2 jobs..

1a: The Firmware options select RAM to allocate to the GPU & Cache, Manually selected! or optimised Settings..

1b: Personally set aside RAM (For GPU & Storage allocation), The GPU being the primary access partner & the Storage cache allocated at the top end of the ram address range..

This allows Dynamic partitioning & Storage can use the RAM, When the GPU does not have a large memory array dynamically allocated..

2a: The RAM is allocated through official Driver & Motherboard Setting GUI & OS, Windows, Linux, Mac..

This is harder because a Free-Ram Allocator has to Dynamically allocate the RAM to the GPU & Cache,

Handled by OS Ram driver..

You can do both? Yes you could!, You could even inform the OS of the allocation so it can use it especially..

Storage & RAM compression by hardware & OS is recommended, ..

Windows LZW, GZip, Deflate, BZip, ZSTD..

Recommended settings are ..

(In Dynamic Cache / RAM Allocations)

Storage first, General Second..GPU Third..

(c)Rupert Summerskill

*****

The rDMA with Zero-Copy concept is one of the most powerful performance boosts of the GPU & Network card market,

Chipset PCIe & Memory channels, Topic, Networking, GPU, Memory & Storage API:

When Motherboard Chipset dependant PCIe bus channels become available...

For direct writing of RAM & Storage & Yes networking the separate PCie channels of the motherboard chipset ..

PCIe Channels add speed to data transfers to & from CPU & internal components..

CPU internal PCIe channels are an evolved & super performant function, This development means a devolution of chipset powers..

Todays base PCIe 5 chipsets are used to CPU internally regulated function & channels..

In the days of the enhanced ports & other classical technology of the early 2000's period..

Enhanced IO, Enhanced DMA access, These features often did not work reliably at high classification rates,

Extended function was reserved for specialised drivers, Windows basic drivers did not & often don't .. Contain features like..

Transfer access..

Enhanced IO, Enhanced DMA

rDMA & Zero-Copy..

Enhanced Function Firmware:

Externally sourced channels from the motherboard chipset & firmware, for general use,..

Improves the performance of all components on the motherboard & in your computer..

Independent chipset functions for GPU + Networking & Storage are hard to make,

Our strategy is to make RAM & Encryption suits available to all internal components ..

PCIe extended channels from the chipset & motherboard enabled & allocated RAM..

Expressly for cache combined with extra ram in the GPU sense (Because that is easy to see),

Unlike the RAM allocated through GPU sidebus combinations..

Well .. Actually, This is ideally seen the way a sidebus or RAM cartridge is seen in a Nintendo 64,

Ideally with memory channel & PCIe Bus access & programming.. & ideally visible in OS kernel details pages & taskbar memory informer..

So with great hardware like the cartridge memory extender & memory compressor,..

Such as seen on the amiga & nintendo & playstation controllers with 64 save slots..

With these methods..

These developments can work & have been seen to work..

(c) Rupert Summerskill

*****

Memory Extension Principles By RS

The cartridge memory extender & memory compressor on the Amiga 1200 & Nintendo 64, ..

For a personal reference, We forward the fact that the Nintendo RAM sits under the cartridge & is first access,

Principle 1, Direct Access RAM:

In this principle we would provide a RAM stick that is directly plugged into the motherboard next to the PCI slot & ideally behind the card, ..

Because then heat from the card fans would not heat up the ram, However..

In my case the RAM would be blown on by the CPU fan! But then i have an Arctic cooler & it is large!

However a RAM stick at the back of the PCIe card would provide direct & uncomplicated RAM that is meant for the GPU or PCIe Card, Such as networking..

With firmware the datalines between the PCIe slot & the RAM are uncomplicated, But we need firmware!

The Graphics card or the motherboard would have to directly enable the RAM & Yes that is pricey!

But lanes would be simple, & If we want.. We can therefore produce method 2..

Method 2:

Further adapting the system of the Direct Connect RAM with PCIe & RAM Lanes..

We connect all the PCIe slots to the Motherboard chipset, With lanes between centrally or off-side located Chipset Processor & RAM..

We can then use an ALU Equivalent processor chip.. to directly & optimally allocate the RAM..

Indeed ALU Allocation Processor is a great feature to have, Because no direct OS control is required..

Control by OS is logical, But ALU does all the DMA & IO & the Input/Output CPU Cache is not flooded..

We can directly manage.. System RAM, Compression & Encryption.. Directly from the feature set..

ALU & CPU Chiplet set, For example the ..

Microsoft Pluton Processor that is on the latest AMD & Intel Chips..

TPM on most motherboards, If fast enough, Could keep the System / RAM & Storage.. encrypted, If we like!

Personally I prefer not to encrypt the storage,.. Too many issues with it..

RAM Encryption is secure & temporary .. Relatively..

But we could!

(c) Rupert Summerskill

*****

The PCI express caching & storage caching will be good, RAM in the 1GB Stick or single chip on the motherboard...

The NVME & Standard harddrive cables, Cache thought, Is good with as little as 20MB,

The most pertinently competitive caching model is around 20MB to 250MB in terms of hard drives..

Datarates between 25MB/s & 540MB/s for SSD directly connected cables,

Caching the direct data fluctuations on throughput is impressive in it's performance..

PCIe 1 4x to 16x PCI5 is sure to appreciate a cache size of 1GB, But even 150MB helps..

Considering the 256Bit Bus, Larger cache array is a requirement, 50MB is good for small aligned data,

Flow thoughput of Zero-Copy data between system ram & GPU & networking adapter.. Are definitely more viable with on PCIe databus access..

Dynamic cache firmware with SVM & Statistical cache &...

Ram.. profiling, Size setting & throughput with latency estimation..

Hardset RAM stick or onboard memory chips

rDMA with Zero-Copy, Although Extended Device RAM is advisably filled with sharable data..

Easy settings profile with advisory & ideally optimal, simple default menu choices..

RAM size = nGB, Onboard RAM division :

Cache,
Network Cache,
Storage Cache,
PCIe & NVME General throughput Cache,
Extended GPU RAM

You can make choices, But we can be clever..

(c)RS

*****

Strategy 2 for cache on motherboards: Expensive motherboards can out compete a cheap CPU,

We mean that a 256KB Cache chip, particularly a small one, .. Could go anywhere!

Our main focus is on 1GB DIMMs, & for the majority of the benefit, That 1GB DIMM is only too reasonable..

For our case in point, Spending on a 16GB Stick that is focused by firmware on GPU & Networking & Storage cache..

In the 1GB for storage, 512MB for a 4 port 1GB/s network card & 14GB for GPU & the rest for general PCIe cache..

Is very high performance!

Higher performance cache chips:

1MB of 16 Way cache on the motherboard.. however .. would remove most of the jitter from PCIe & rDMA..

We do have to point out in these 2 strategies, The client is wealthy..

The 16GB DIMMs are an example! Very large! But then .. It's for the GPU mainly!

The cache chips are quite good at alleviating threading issues on PCIe Bus..

But cache chips are small, So the motherboard would need quite a bit of hightech small stuff to have it..

We may point out that in both cases, Performance is ABSOLUTE.. Like the Vodka..

Having both of them? But then, How wealthy are our clients ?

(c) Rupert Summerskill

*****

Architectural concept for optimizing memory access and reducing CPU overhead,..

By prioritizing direct hardware channels and dynamically managing cycles, ..

This design effectively tackles common latency bottlenecks found in high-throughput workloads.

Ccore mechanics outline, in the Layered PCB and Dynamic Memory Access proposal:

Core Architecture and Routing:

Direct Access Channels: The motherboard utilizes a specialized array to provide direct, independent access to PCIe and RAM channels.

CPU Offloading: These channels are designed to interface directly with the GPU, Storage, RAM, and CPU without placing an unnecessary processing burden on the CPU itself.

Latency and Cycle Management:

Dynamic Cycle Allocation: The system dynamically allocates PCIe and DRAM Cycle 2, reserving Cycle 1 strictly for situations when the system is flooded with requests.

Peak Bandwidth: This cycle management strategy ensures that the primary CPU, GPU, and RAM component threads have priority access to peak bandwidth, effectively minimizing system latency.

Dual-Layered Resource Allocation:

The design splits the memory allocation responsibilities between the motherboard's firmware and the operating system:

Firmware/BIOS Level: The BIOS handles manual or optimized RAM allocation for the GPU and Cache..

It also reserves RAM specifically for GPU and Storage allocation, placing the storage cache at the top of the RAM address range to allow for dynamic partitioning.

OS/Driver Level: The operating system (Windows, Linux, or Mac) uses a Free-Ram Allocator via its RAM driver to dynamically assign memory to the GPU and Cache.

Hardware & OS Collaboration: The system can inform the OS of these allocations for specialized use,

Recommending hardware and OS-level compression standards like LZW, GZip, Deflate, BZip, or ZSTD.

Priority Hierarchy:

For dynamic cache and RAM allocations, the architecture enforces a strict priority order: Storage takes first priority, General tasks take second, and the GPU is placed third.

Minimizing CPU overhead via direct PCIe routing is an excellent strategy for workloads that require massive, uninterrupted memory bandwidth..

Such as running localized machine learning reconstructors, managing spatial-temporal upscaling, or processing real-time global illumination pipelines.

Planning:

Mapping out the physical PCIe lane configurations for a custom board to test this, & planning to simulate this dynamic allocation logic in a software environment ...

(c)RS

*****

Full Development Strategy Guide..

The Dual-Layered Allocator (BIOS + OS): Strategy:

The concept of splitting allocation between the BIOS and the OS is the key to dynamic partitioning.

Currently, technologies like Resizable BAR allow the CPU to map the entire GPU VRAM into system memory..

The architecture flips this: the BIOS actively provisions top-address system RAM to the GPU and Storage.

For the OS side, the Free-Ram Allocator driver would need to sit highly privileged in the kernel,..

When the storage controller pulls compressed data (using Windows LZW, GZip, or ZSTD), ..

The hardware decompresses it directly into this dynamically partitioned RAM block..

Enforcing your strict priority hierarchy (Storage > General > GPU) at the driver level ensuring that the cache never stalls during heavy rendering or upscaling tasks.

....

Bypassing the CPU root complex to eliminate latency bottlenecks is exactly the trajectory high-throughput computing needs to follow..

The Layered PCB concept takes the principles of Peer-to-Peer Direct Memory Access (P2P DMA, P2P rDMA & HPC), ..

Pushing them further by enforcing strict hardware-level cycle arbitration and a dual-layered firmware/OS allocation scheme.

This approach is highly effective for workloads starved for uninterrupted memory bandwidth, Especially when managing localized machine computation,..

General Computation & Gaming, CAD & so forth .. ML, ...

Learning reconstructs, spatial-temporal upscaling, or massive global illumination pipelines.

& So forth..

....

Mapping the Physical PCIe Lane Configuration:

To achieve direct channels between the GPU, Storage, and RAM without burdening the CPU, The custom motherboard requires a PCIe Switch Topology (utilizing chips like a Broadcom PLX switch).

In a standard architecture, most endpoints route through the CPU's Root Complex..

Examples that don't directly obey CPU only functions:

Firmware & Motherboard Off-Chip PCIe, DMA & IO, Chipset lead .. DMA, rDMA, Enhanced IO, Device-Lead Dynamic Polling,

Example System PlayStation 5 Compression & Storage Chipset, + XBox, VIA System Motherboards & So on..

....

In a layered design, the devices must sit downstream of a dedicated PCIe switch on the motherboard.

Upstream Port: Connects to the CPU Root Complex (handling standard OS tasks and Cycle 1 overflow).

Downstream Ports: You map independent lanes (e.g., x16 to the primary GPU, x4 to the NVMe storage, and x4 to any localized edge inference hardware like an EdgeTPU or Movidius X).

Routing Logic: When the GPU requests data from the storage cache, the PCIe switch intercepts the transaction and routes it directly to the NVMe drive..

The CPU does not need to see the data packet directly, Eliminating CPU overhead.

....

Gem5 Simulation & Firmware Creation: Testing:

Reasoning & Development, With additional suggested usage, SVM, ECC Elliptic Curve.. SVM Emulation..

For statistics &.. Dynastic Centripetal Motion, In other words .. The Butterfly Effect in balance..

....

Simulating the Dynamic Logic : Gem5

Before printing a highly expensive custom silicon board, ..

The cyclic stable levels architecture can be thoroughly tested in a software simulation environment.

gem5 is the industry standard for cycle-accurate computer architecture simulation..

You can use it to build a virtual motherboard and test your cycle management strategy:

Custom Memory Controllers:

You can write a custom memory controller script in Python/C++ within gem5 to enforce your rule:

allocate DRAM Cycle 2 dynamically to the GPU/Storage, and reserve Cycle 1 only for flooded requests.

Simulating the OS Driver: You can boot a full, unmodified Linux kernel inside gem5..

By writing a custom kernel module to act as your "Free-Ram Allocator,",

You can track exactly how much latency is saved when the driver bypasses the CPU and requests memory directly from your simulated Direct Access Channels.

....

Phase 1: Custom Topology & Memory Controllers (gem5) PCIe Switch Implementation:

The virtual motherboard requires a simulated PCIe Switch Topology to route transactions directly, Ensuring the CPU never sees the data packets..

Actually you may poll the CPU with updates, Because the objective of creating additional routs.. Is not to blind the CPU!

After-all the CPU is the user & OS!

Lane Mapping: Downstream ports need to be configured with independent lanes, such as x16 for the primary GPU and x4 for NVMe storage..

Additional x4 lanes can be mapped for localized edge inference hardware, including the EdgeTPU or Movidius X..

Cycle Arbitration: A custom memory controller script, written in Python/C++, must be implemented to enforce the dynamic allocation of DRAM Cycle 2 ..

To the GPU/Storage, strictly reserving Cycle 1 for flooded requests..

Structuring cleanly isolated Python environments on the Windows & Linux host will make managing the gem5 build dependencies and these custom C++ bindings much more efficient during iteration.?

Phase 2: Firmware & BIOS Emulation, .. Top-Address Provisioning: The simulated BIOS must be programmed to actively provision the top-address system RAM..

To the GPU and Storage, flipping the standard Resizable BAR methods with additional utility..

Dynamic Partitioning: This setup ensures that Storage can dynamically use the RAM when the GPU does not require a large allocated memory array.

Phase 3: The Free-Ram Allocator (Windows & Linux Kernel Module),.. Privileged OS Driver:

A custom kernel module must be written to act as the Free-Ram Allocator, sitting highly privileged within the OS..

Decompression & Allocation: When the storage controller pulls compressed data (utilizing standards like LZW, GZip, Deflate, BZip, or ZSTD), ..

The hardware will decompress it directly into the dynamically partitioned RAM block..

Strict Hierarchy Enforcement: The driver must dynamically manage these allocations by enforcing the priority order: Storage first, General tasks second, and the GPU third..

Phase 4: Workload Validation .. Latency Tracking:

By booting a full, unmodified Linux kernel inside gem5, you can track the exact latency reductions achieved when the Free-Ram Allocator bypasses the CPU root complex..

Simulation Targets: Validating the architecture with high-throughput workloads..

Such as SVM emulation, spatial-temporal upscaling, or massive global illumination pipelines, will prove the efficacy of the direct access channels..

When eventually moving this from simulation to a physical hardware array, integrating a specialized power and thermal management firmware ..

Clever Firmware will be critical to support the sustained peak bandwidth draws across the components

(c)RS

*****

NTP + PTP + Networking & JIT:

On the topic of JIT, Sharing & Timer values, PTP & NTP ( Audio, Video, Gaming, Science )

https://science.n-helix.com/2026/08/power.html

https://science.n-helix.com/2022/01/ntp.html

https://science.n-helix.com/2023/06/ptp.html

https://science.n-helix.com/2022/08/jit-dongle.html

https://science.n-helix.com/2022/06/jit-compiler.html

Power - Graph + Core Sophisticated PSU & Power Grid design elements with Power (Watts & Volts) / Thermal & Control

Power Control the PSU, A special Graph (c)RS

Core Sophisticated PSU & Power Grid design elements with Power (Watts & Volts) / Thermal & Control:

Power Control & PSU, A special Graph message illustrating an advancement on the the already revealed NPU & MCU Power-Control Firmware By RS

Now as you know, I wrote about Power Control for PSU & Power Grids,

Now Graphs, We know clever PSU illustrate the power levels on the PC appliance app & yes this is clever! :p & Expensive, Right?! Very Expensive..

Now i have an AX1000 WATT PSU by corsair, Refurbished & good, Apart from the fan..

Graphs traditionally show a flat graph in 2D, To the same level as AMD GPU Driver thermal panel,

These graphs are useful & simple enough to understand, 3D graphs are fly & all that, But they hardly improve on the knowledge you receive..

We could ofcourse use 3D Graphs, 2D & 3D Jacobian Graphs, SVM & Statistical maths..

The PSU & the GPU,.. Processors & motherboard style VThermal Illustrative graphs..

The power control itself is what we are after automating, So we are going to create a short list for improving the internal control..

Internal control MCU Control with SVM

With SVM & Elliptic Curve Maths Emulated SVM (Graph Fitting), We can optimise the graphs against benchmark database, MicroDB..

So PSU & GPU don't have internal storage for dynamic databases, What do we do?

Database RAM Cache, Yes even 15KB!, Databases can be small, 8KB..

ENV : Arrays, We could use environment arrays stored in OS RAM? Basically an IndexDB

We want to control the Fan & the Vthermal & VRM? What to do?

SVM & Jacobian Graph, Fitting, This means ML, Programming or data analytics engineering.

(c)Rupert Summerskill

Example IndexDB Set:

// Reference Dataset for model with LightML + SVM + ECC 3D Manifold Regression

// Standard of Drift in NTP & PTP

Network + NTP + PTP + gPTP Time Server

+ Resolver Data + Data Plane

(
LightML + SVM + ECC 3D Manifold Regression = (

( Local Drift File Data );
( Global Drift File Data );
( GNSS Relativistic Corrections );
( Gravity Model );
( Fibre Dispersion Model );
( Network Bandwidth Model );
( Network Latency Model );
( Network Protocol Model );
( Network Routing Model );
( Master Security Model );
( Network Security Model );
( Application Security Model );

),

LightML + SVM Resolve → Consensus Time Table

);

//(c)RS

We need power control in it, That's it!

// Reference Dataset for Hardware Power & Thermal Control
// Applying LightML + SVM + ECC 3D Manifold Regression for PSU/GPU Automation
// (c) Rupert Summerskill

// Standard of VThermal, VRM Stability & Acoustic Output

Power Control + PSU + GPU + VRM Automation

+ Sensor Resolver Data + Power Plane

(

LightML + SVM + ECC 3D Manifold Regression = (

( Local Transient Load Profile ); // Real-time current (Amps) spikes
( MicroDB Cache State ); // The 8-15KB Dynamic RAM Cache from OS
( Vthermal Resistance Model ); // Heat saturation over time (Heatsink capacity)
( VRM Efficiency Curve ); // Voltage regulation mapping (Sweet spots)
( Fan Acoustic/Thermal Jacobian ); // 3D Matrix: rate of temperature change vs. RPM
( Component ACPI Power State ); // Sleep/Wake/Boost signals from the motherboard
( Input Voltage Ripple Model ); // PSU AC-side grid stability & capacitance
( Predictive Power State Drift ); // Expected load based on historical SVM vectors

),

LightML + SVM Resolve → Consensus Power & Thermal State

);

//(c)RS

How the Math Works in the Firmware

By structuring the firmware this way, you are fundamentally changing how the hardware operates:

The Jacobian Graph Fitting: Instead of a flat 2D fan curve (e.g., "If 60°C, then 50% fan speed"),

the Jacobian matrix calculates the rate of change. If the GPU spikes by 10°C in one second,

The firmware knows a massive load just hit and ramps the VRM and fan up before the thermal limit is breached.

SVM and ECC on 15KB: Support Vector Machines are perfect for this because once the model is trained, ..

The resulting vectors (the boundaries of what is considered "optimal power") take up virtually no memory..

You don't need to store a massive database of past temperatures on the PSU,..

You just store the boundary equations in that 8KB–15KB cache.

The IndexDB Bridge: The heavy lifting, storing the historical benchmarks and large datasets.. Lives in the OS RAM..

The software feeds only the highly compressed, refined LightML weights down to the PSU/GPU MCU via USB or PCIe headers.

This gives you a system that constantly adjusts its own efficiency curve, maximizing power delivery while keeping acoustic noise to an absolute minimum.

(c)RS

*****

Core Sophisticated PSU & Power Grid design elements with Power (Watts & Volts) / Thermal & Control: Design Elements...

1. Conceptual architecture

Layers:

OS Layer (Heavy ML + IndexDB):

Role: Train, refine, and compress models; maintain historical datasets.

Storage: OS RAM + disk; IndexDB-style micro-DBs per device (PSU, GPU, VRM).

Output: Tiny LightML/SVM parameter sets + ECC manifold coefficients, pushed down as “profiles”.

MCU Layer (Reflex Engine, 8–15KB Cache):

Role: Real-time control loop for fan, VRM, and power state.

Storage:

Boundary equations: SVM hyperplanes, Jacobian coefficients, ECC manifold parameters.

MicroDB cache: A few recent state vectors + profile metadata (version, checksum, validity window).

Sensor/Power Plane:

Inputs: Temperature (GPU, VRM, PSU), current draw, voltage ripple, ACPI power states, transient load spikes.

Outputs: Fan RPM, VRM voltage/current limits, PSU rail behaviour, “soft” power caps.

2. The IndexDB / MicroDB model

OS + Firmware (USB Stick or Flash Card Port, ideal for big data), IndexDB:

Tables (conceptual):

thermal_events:
Fields: timestamp, device, temp_before, temp_after, load_vector, fan_rpm, VRM_state.

power_transients:
Fields: amps_spike, duration, rail, ripple, resulting temp delta.

profiles:
Fields: profile_id, device_type, SVM_params, ECC_params, Jacobian_matrix, validity_range.

Job:

Aggregate events → train LightML/SVM + ECC manifold.

Compress to boundary equations + Jacobian matrices.

Emit profile blobs sized to fit 8–15KB MCU cache.

MCU-side MicroDB cache (8–15KB):

Layout (example):

Header (64–128B): profile_id, version, CRC, timestamp, device mask.

SVM block (~2–4KB): support vectors + coefficients (heavily quantized, e.g. INT8/INT4).

Jacobian block (~2–4KB):

Fan acoustic/thermal Jacobian

VThermal resistance coefficients

VRM efficiency curve segments.

ECC manifold block (~2–4KB): compact curve parameters for non-linear regions.

Scratch state (~1–2KB): last N state vectors (e.g. 8–16 samples) for drift estimation.

3. Control loop in firmware

3.1 Input vector

Every control tick (e.g. every 10–50ms), MCU builds:

State vector x:

T_gpu – current GPU temp

T_vrm – VRM temp

T_psu – PSU internal temp

I_load – instantaneous current draw

dT_gpu/dt – temp rate of change

ACPI_state – S0/S3/S5, boost flags

V_ripple – input voltage ripple

profile_id – active profile selector (optional bitmask)

3.2 Jacobian fan/VRM response

Instead of a static curve:

Jacobian matrix J approximates:

Δ𝑢=𝐽⋅Δ𝑥

where:

Δ𝑥 = change in state (e.g. +10°C in 1s, +20A spike)

Δ𝑢 = change in control outputs (fan RPM, VRM margin, soft power cap).

Example behaviour:

If 𝑑𝑇𝑔𝑝𝑢/𝑑𝑡 is high, even if absolute temp is “safe”, firmware pre-emptively:

Boosts fan RPM aggressively for a short window.

Tightens VRM limits to avoid overshoot.

This is the “anticipatory” part we describe, Reacting to rate not just level.

3.3 SVM boundary check

SVM model: defines “optimal region” in state space:

Region A: Silent/eco

Region B: Balanced

Region C: Performance/boost

Region D: Protection/derate

MCU evaluates:

𝑦=sign(∑𝑖𝛼𝑖𝐾(𝑥,𝑥𝑖)+𝑏)

with a very small set of support vectors ..

𝑥𝑖, quantized coefficients 𝛼𝑖, and simple kernel (e.g. linear or low-order polynomial).

Result:

Selects which policy to apply to Jacobian outputs:

In Silent region: cap fan RPM, allow slightly higher temps.

In Performance region: allow higher VRM output, more aggressive fan.

In Protection region: hard limits, ramp fan, possibly signal OS to throttle.

3.4 ECC manifold for non-linear zones

ECC manifold: used where behaviour is strongly non-linear:

Near thermal saturation of heatsinks.

At VRM efficiency “knees”.

Under unstable input ripple.

MCU uses small ECC curve parameters to warp the Jacobian/SVM outputs in those zones, e.g.:

Curve: maps “requested fan RPM” to “actual effective cooling” based on VThermal saturation.

Prevents overconfidence in fan ramps when heatsink is already saturated.

4. Power control integration

We sketch the secondary dataset; here’s how it plugs in:

Local Transient Load Profile:

Short window buffer (e.g. last 1–2s of I_load, T_gpu, T_vrm).

Used to compute

𝑑𝑇𝑑𝑡, spike detection, and feed Jacobian.

MicroDB Cache State:

Holds current profile + a few recent state vectors.

Enables Predictive Power State Drift: “we’ve seen this pattern → expect boost soon”.

Component ACPI Power State:

If OS signals upcoming boost (e.g. game launch, render job), MCU can pre-warm VRM and fan.

Input Voltage Ripple Model:

If grid/AC-side is unstable, firmware can slightly lower the (Volt to WATT) vs VRM rate power or..

Adjust VRM behaviour to protect components, Aka..

Core Sophisticated PSU & Power Grid design elements:

Thermal capping, Power levelling, Surge Protection..

Capacitor Buffering spikes & auto de-levelling power fluctuations in dynamic power versus VThermal, Requirements & Graphed Optimums.. 

5. Data path: OS ↔ PSU/GPU

Transport options:

USB HID / vendor-specific: for PSU.

PCIe sideband / SMBus / I2C: for GPU/VRM.

Protocol idea:

Profile Update Frame:

Header: device_id, profile_id, version, size, CRC.

Payload: SVM block, Jacobian block, ECC block.

Flags: “safe to apply live” vs “apply on next idle”.

(c)RS

*****

PTP as part of Time Related Power Management, By RS

Synx, PTP & NTP, Timer Synchronisation, NV Fence mode, DMA Fence & So forth..

Now my personal thought on timer synchronisation goes way back, But the power saving is not the primary consideration of my development..

I have aimed at obtaining a level of Voice + Video or 3D Synchronisation, Even in gaming on consoles such as XBox 360, PS1 & Amigas ..

Timers where off, Not always, But a badly written frame-sync in a demo scene https://scene.org demo in archive, ..

Audio vs Video.. Could be out of synchronisation for a frame or two, ..

PTP Synchronisation is mainly aimed at IO & DMA with heavy Processor usage, Such as a game, Video Rendering or live streaming server..

Timer states for Economy Mode Power & Sleep Cycling is a new one, But quite logical ..

Processor Rest states are defined by economising on data throughput over time, & ..

Applying Synchronisation Over Time to the Rules of Processor Rest States.. 

Allows Power Averaging & Economising Efficiency, By ordering packet processing..

var ('SA') = (PTP = ('PTa, PTb, PTc, PTd, ..PTn');

For Synchronising Data = ('SA');(

var Packet List Array ('PLA') = ('Pa, Pb, Pc, Pd, ..Pn');

var 'Sort' = ('SA') + ('PLA')

output 'PTPList' = 'SortPTP'

);

// https://www.phoronix.com/news/Synx

//(c)RS

PTP as a power aware scheduler & synchroniser, The Concept:

PTP/NTP/gPTP don’t just align clocks, they can align work.

If IO, DMA, GPU, CPU, and NV/DMA fences all share a precise time base, then:

Packets, frames, and jobs can be batched into time slots.

Rest states (C‑states, P‑states, sleep cycles) can be aligned to idle windows.

Power draw becomes time‑averaged and smoothed, not spiky.

So PTP becomes a temporal spine for both:

Media synchronisation (voice, video, 3D, gaming), and

Power management (economy mode, sleep cycling, packet ordering).

...

NV fence, DMA fence, Synx: why they matter here:

NV fence / DMA fence / Synx fits perfectly:

Fences already define ordering constraints in GPU/CPU/IO pipelines.

If those fences are PTP aware, then:

Work can be scheduled at specific time slots, not just “after X”.

You can align frame presentation, audio buffers, and power ramps.

So the model becomes:

Fence graph + PTP timeline → time‑ordered dependency DAG.

That DAG feeds both:

Media sync (no more “audio vs video off by a frame or two”), and

Power sync (no more chaotic spikes).

...

How it ties into PSU/MCU power architecture:

If we plug this into your previous Jacobian/SVM/ECC power control:

PTP‑aligned workload → more predictable transient load profiles.

MCU sees regular patterns instead of random spikes:

Easier to compute

𝑑𝑇/𝑑𝑡 and predict boosts.

Better Predictive Power State Drift.

OS can signal:

“Upcoming burst at PTc–PTf” → pre‑warm VRM/fan.

“Idle window at PTg–PTj” → deepen rest states.

So PTP isn’t just for cluster time sync—it becomes a temporal contract between:

OS scheduler

IO/DMA/GPU pipelines

PSU/GPU MCU reflex engine.

(c)RS

*****

Future Anticipation & Action to control ( PSU Power transience over(/) time) With Work Done Motivation By RS

Thermal Inertia Prediction Layer:

A tiny ML block that predicts future heatsink saturation based on current dT/dt + airflow + VRM load → improves anticipatory control.

So a future statistics saturated database, So how do we handle the DataBase in ram becoming saturation heavy..

On a tiny ram footprint

The answer here...

.....

The Thermal Inertia Prediction Layer (TinyML Version):

What it predicts

future heatsink saturation

future cooling rate

future VRM thermal load

future dT/dt behaviour

What it does not store
historical temperature logs

airflow logs

VRM load logs

transient load history

All of that stays in IndexDB on the OS side.

The Trick: Replace “Database” With a Micro‑Model:

Instead of storing a database, you store a 3–6 coefficient predictive function.

.....

This is the same trick used in:

TinyML

emlearn

TFLite Micro

embedded control loops

telecom timing firmware

The model looks like this:

The Mathematical EngineThe core of this firmware relies on replacing static logic with dynamic, predictive mathematics..

Anticipatory Response (Jacobian):

Instead of waiting for a thermal threshold to be breached,..

The firmware calculates the rate of change using a Jacobian matrix approximation..

This allows the system to preemptively ramp up the VRM and fans if a massive load hits suddenly.

The PSU Future.. control model looks like this:

Δ𝑢=𝐽⋅Δ𝑥

Optimal State Evaluation (SVM):

The MCU uses Support Vector Machines to evaluate "optimal regions" (such as Silent, Balanced, Performance, or Protection) in the state space.

MCU evaluates:

𝑦=sign(∑𝑖𝛼𝑖𝐾(𝑥,𝑥𝑖)+𝑏)

&...

Thermal Inertia Prediction:

𝑇𝑓𝑢𝑡𝑢𝑟𝑒=𝑎1⋅𝑑𝑇/𝑑𝑡+𝑎2⋅𝑇𝑐𝑢𝑟𝑟𝑒𝑛𝑡+𝑎3⋅𝑎𝑖𝑟𝑓𝑙𝑜𝑤+𝑎4⋅𝑉𝑅𝑀𝑙𝑜𝑎𝑑+𝑏

Where:

𝑎1,𝑎2,𝑎3,𝑎4 are INT8 coefficients

𝑏 is a bias term

total size: < 64 bytes

This replaces megabytes of historical data.

.....

How MCU Uses It

Every control tick:

Compute dT/dt

Read airflow sensor

Read VRM load

Apply predictor:

𝑇𝑓𝑢𝑡𝑢𝑟𝑒=𝑓(𝑑𝑇/𝑑𝑡,𝑎𝑖𝑟𝑓𝑙𝑜𝑤,𝑉𝑅𝑀)

Feed predicted temperature into:

Jacobian

SVM region selector

ECC manifold

Giving the MCU anticipatory control without needing a database.

(c)RS

*****

NTP + PTP + Networking & JIT:

On the topic of JIT, Sharing & Timer values, PTP & NTP ( Audio, Video, Gaming, Science )

https://science.n-helix.com/2026/08/firmware.html

https://science.n-helix.com/2026/08/power.html

https://science.n-helix.com/2022/01/ntp.html

https://science.n-helix.com/2023/06/ptp.html

https://science.n-helix.com/2022/08/jit-dongle.html

https://science.n-helix.com/2022/06/jit-compiler.html

*****

Chips & Clocks : 'The Power To Act'

They produce chipsets..

Microchip, Nokia & So forth..

References also found in my NTP & PTP docs..

https://science.n-helix.com/2022/01/ntp.html

https://science.n-helix.com/2023/06/ptp.html

RS

https://www.microchip.com/en-us/solutions/industrial/smart-energy-metering

https://safran-navigation-timing.com/

https://news.siemens.com/fr-ca/this-solution-protects-electrical-substations-from-timing-disruptions/

****

Very High Precision Clocks, Reference Data: PTP & NTP, Video & Audio Synchronisation,..

If you think that is bizarre, Look at VESA & HDMI, NIST or CERN for high valuation on precision timing.

RS

https://safran-navigation-timing.com/product/tiqker/

https://www.microchip.com/en-us/products/clock-and-timing/components/atomic-clocks/atomic-system-clocks

8Bit Inferencing & computation & Arrays of 8Bit SiMD instructions, By RS

8Bit Inferencing & computation & Arrays of 8Bit SiMD instructions, By RS

Yes Intel & AMD & Coral Edge TPU & like-minded instructions for parallel array processing:

Well defined Bundled 8Bit Parameterisation:

Firstly as stated in documents by myself before the RGB+BW 8,8,8,8 colour system developed by myself is a first rate utility to process 8Bit defined colours in HDR,

You can use 4,4,4,4 & any array of 8Bit precision or lower colour definition, For Planar Textures.

Secondly, Machine learning, defined in 8Bit is not beyond the capacities of Man's brain to define!

Most Humans & some types of animals think at a base level in 8Bit, Humans bundle 8Bit into higher precision, such as eyes.. & so forth..

Squid & Octopuses bundle _bit, Upto 96Bit Colours! So yes the system is well defined!

So you can think about 8Bit bundling as a very early thinking life-form evolutionary system for advancement..

You do need to define parameters affected by 8bit,.. With care,

With matrices of memory arrays, In higher definition, 8Bit weights may seem effective, 8bit maths may seem effective!

But we do need to optimise!

So there are Weight & Parameter machine learning models that are parametrized in 2Bit & upto 16Bit (in most cases),

We could use 64Bit & 32Bit, The CPU is a case point, where this matters.

So there are a lot of functions to consider, Work & thought are required, This is most important..

Remember Buddha & mentalists, Mathematicians, Physicists & Scientists & Psychologists & biologists, Optimise this path.

(c)Rupert Summerskill

*

Core ideas:

Main thesis: Practical, high‑performance ML inferencing and image/video processing can be built around low‑bit (4–8 bit) representations and SIMD/AVX/NPU arrays,..

With careful tiered precision, compression, and memory alignment to preserve accuracy while massively improving throughput and power efficiency.

Key themes: 8‑bit as a sweet spot for human‑like inference; quantization strategies (4→8→16→32 bit); packed‑bit SIMD math,..

Tiered caching and transparent precision casting; matrix/AVX/TPU mapping; wavelet/brotli compression for tensors,..

hardware choices (EdgeTPU/Coral, Movidius, Hailo, AVX/Intel/AMD).

Applied domains: image upscaling/edge detection, HDR/WCG color handling, medical imaging (ResNet‑style detection), low‑power edge inference, and database/statistics preprocessing for ML.

Architectural recommendations: use aligned memory blocks (8×8, 16×16), local DMA and 64–128‑byte cache-friendly transfers, prefetching, loop unrolling, and micro‑kernel dequantization for FP16/FP32 when needed.

Practical implementation checklist (engineer‑ready):

Model preparation

Train or fine‑tune in FP32/F16; export to ONNX/TFLite.

Apply post‑training quantization to INT8; evaluate AWQ/AWQ‑like methods for 4‑bit activation/weight cases.

Keep a small FP16 “remainder” path for critical layers (first/last, attention heads).

Tiered runtime

Load stage: read tensors in higher precision (F32/F16) for sorting/selection.

Cache stage: compress with Brotli‑G or wavelet autoencoder for large tensors; store compressed blocks in RAM.

Infer stage: decompress into INT8/INT4 packed buffers; run SIMD/TPU kernels.

Dequantize stage: when needed, run a fast dequant kernel to FP16 for layers that require float remainder.

Memory & packing

Use packed layouts: 32‑bit = 4×8b, 64‑bit = 8×8b, 128‑bit = 16×8b.

Align DMA transfers to cache line sizes (64B) and GPU bus widths (128/256/512 bits).

For add/mul chains, reserve a small extra bit per lane (carry/guard) to avoid overflow in packed arithmetic.

Hardware mapping

Edge/embedded: Coral EdgeTPU (INT8), Movidius (INT8), Hailo (TOPs) — use for low‑latency, low‑power inferencing.

Desktop/server: AVX2/AVX512 SIMD for packed INT8/INT16; use dp4a/dot‑product intrinsics where available.

Hybrid: Offload matrix multiplies to NPU/TPU and keep control/branching on CPU; use local DMA to avoid CPU/GPU thrash.

Algorithmic optimizations

Depthwise separable convs (DS‑CNN) and BNN/TNN for extreme compression.

Use wavelet autoencoders to compress repetitive patterns before quantization.

For edge detection/upscaling: combine small fixed‑point SiMD kernels (fast) with occasional float refinement passes.

RS

*

Summary of goals for document

We argue that 8‑bit parameterization is a principled design space—useful for color pipelines, texture formats, and ML inference,..
Rather than a mere optimization hack..

You want practical, system‑level ways to make 8‑bit (and nearby low‑bit) computation reliable: quantization strategies, parameter sensitivity, hardware mapping (SIMD/TPU/GPU), and perceptual/functional metrics that guide when to bundle or expand precision.

---

Recommended deliverables


| Option | Purpose | Key outputs |
|---|---:|---|

| A — Formalize 8‑bit sensitivity metrics | Quantify how model outputs change with bit reductions | Definitions; formulas for sensitivity; test harness; example results on a small model |

| B — Map perceptual error to quantization noise | Tie visual/ perceptual metrics to numeric quantization choices for textures/HDR | Dataset list; experiments (PSNR/SSIM/LPIPS); mapping curves; decision thresholds for 4:4:4 vs 4:2:2 |

| C — Reference 8‑bit inference pipeline | End‑to‑end blueprint for deploying 8‑bit inference on SIMD/TPU/GPU | Quantization scheme; accumulation rules; mixed‑precision policy; calibration steps; code sketch and test plan |


---


Concrete plan for Option C — Reference 8‑bit inference pipeline

1. Goals and constraints

- Target: deterministic inference with minimal accuracy loss vs FP32 baseline.
- Hardware: SIMD (x86/ARM), Coral Edge TPU, GPUs with 8‑bit matrix ops.
- Workloads: CNNs for image tasks, transformer blocks for small language/vision models, planar texture transforms.

2. Quantization primitives and notation

- Quantize a real tensor \(x\) to \(k\)-bit integer \(q\) using scale \(s\) and zero point \(z\):
\[
q = \mathrm{clip}\left(\left\lfloor \frac{x}{s} \right\rceil + z,\; q_\text{min},\; q_\text{max}\right)
\]
where \(q_\text{min}=0,\; q_\text{max}=2^k-1\) for unsigned, or symmetric signed range for signed formats.
- Dequantize:
\[
\hat{x} = s \cdot (q - z)
\]

3. Per‑tensor vs per‑channel

- Per‑channel scales for weights in convolution/linear layers reduce bias from heterogeneous distributions.
- Per‑tensor scales for activations are cheaper but require robust dynamic range control (clipping or activation folding).

4. Accumulation and mixed precision

- Accumulate in at least 32 bits for large dot products to avoid overflow and preserve dynamic range; where hardware supports, use 16→32 accumulation with compensated summation.
- Mixed precision policy:
- Weights: 8‑bit per‑channel symmetric quantization.
- Activations: 8‑bit asymmetric per‑tensor with dynamic range calibration.
- Biases and layernorm/softmax internals: 32‑bit float or 16‑bit float depending on sensitivity.
- Final logits and softmax: 32‑bit or 16‑bit to preserve numerical stability.

5. Calibration and clipping

- Calibration pass: run representative data through model to collect min/max or percentile ranges (e.g., 99.9th percentile) for activations.
- Clipping strategies: use percentile clipping or learned clipping parameters (PACT) to reduce outlier impact.
- Zero‑point handling: prefer symmetric quantization for weights; asymmetric for activations when zero offset matters.

6. Training vs post‑training

- Post‑Training Quantization (PTQ): fast, good for many models with calibration; include bias correction and per‑channel weight scaling.
- Quantization‑Aware Training (QAT): emulate quantization during training (fake quant) to recover accuracy for sensitive models; use straight‑through estimator for gradients.

7. Rounding and stochasticity

- Deterministic rounding (nearest, tie to even) for reproducibility.
- Stochastic rounding can help during training to avoid bias but complicates deterministic deployment.

8. Error metrics and validation

- Functional metrics: task accuracy, top‑k, BLEU (NLP), mAP (detection).
- Visual metrics for textures/HDR: PSNR, SSIM, LPIPS, and perceptual thresholds mapped to bit choices.
- Numerical drift checks: layerwise output difference norms; cumulative KL divergence of logits.
- Acceptance criteria: e.g., <0.5% absolute accuracy drop or LPIPS < threshold for visual tasks.

9. Hardware mapping and SIMD considerations

- Pack layout: align tensors to vector lanes; use blocked layouts that match SIMD width (e.g., 16 or 32 lanes).
- Memory layout: planar textures benefit from contiguous channel packing for vector loads; prefer 4‑channel packing for RGBA-like operations.
- Chroma sampling: prefer 4:2:2 over 4:2:0 for ML pipelines where chroma fidelity affects model outputs.

10. Test harness and experiments

- Unit tests: quantize/dequantize roundtrip, accumulation overflow tests, per‑channel vs per‑tensor comparisons.
- Benchmarks: latency, throughput, memory footprint, energy per inference.
- A/B experiments: PTQ vs QAT; symmetric vs asymmetric; accumulation bitwidth 16 vs 32.

---

Quick experimental recipes (ready to run):

1. Layer sensitivity sweep
- For each layer \(L\), quantize only \(L\) to 8‑bit (others remain FP32). Measure task metric drop. Rank layers by sensitivity.

2. Activation clipping ablation
- Compare min/max, 99.9th percentile, and learned clipping (PACT). Plot metric vs clipping percentile.

3. Per‑channel vs per‑tensor
- Compare accuracy and memory overhead; report per‑layer improvement.

RS

*


// Code path for Tensor-Flow & ONNX 32Bit & 8Bit:
// Conceptual conversion down:

load_model(path) -> model_fp32
preprocess(input) -> input_fp32

// Tiered cache & quantize

if (should_compress(input_fp32)) {
compressed = brotli_g_compress(input_fp32)
store_in_cache(compressed)
input_fp32 = brotli_g_decompress(compressed)
}

input_int8 = quantize_to_int8(input_fp32, scale, zero_point)
pack_buffer = pack_8bit_to_u32(input_int8) // 4x8b -> u32 lanes

// Run SIMD/TPU kernel

result_packed = run_simd_dot_product(pack_buffer, model_int8_weights_packed)

// Optional dequantize for final layers

result_fp16 = dequantize_to_fp16(result_packed, scale)
final = run_fp16_refinement(result_fp16, last_layer_fp16)
postprocess(final)

// (c)RS

*


// Testing of the Image Inference Bit Depth 8Bit & 32Bit with results : RS
// Multiple selection paths, With ONNX & TF

// Conversion down : hardware choices (EdgeTPU/Coral, Movidius, Hailo, AVX/Intel/AMD).

#!/usr/bin/env python3
"""
onnx_to_int8_edgetpu_prototype.py
Usage examples at bottom.
"""
import sys, os, time, argparse, glob
from pathlib import Path

# Lightweight optional imports with helpful messages
missing = []
try:
import onnx
except Exception:
onnx = None; missing.append("onnx")
try:
import onnxruntime as ort
except Exception:
ort = None; missing.append("onnxruntime")
try:
from onnxruntime.quantization import quantize_static, CalibrationDataReader, quantize_dynamic
except Exception:
quantize_static = quantize_dynamic = CalibrationDataReader = None; missing.append("onnxruntime.quantization")
try:
from onnx_tf.backend import prepare as onnx_tf_prepare
except Exception:
onnx_tf_prepare = None; missing.append("onnx-tf")
try:
import tensorflow as tf
except Exception:
tf = None; missing.append("tensorflow")
try:
import numpy as np
from PIL import Image
except Exception:
np = None; Image = None; missing.append("numpy/Pillow")
try:
from pycoral.utils.edgetpu import make_interpreter
from pycoral.adapters import common, classify
except Exception:
make_interpreter = None; missing.append("pycoral/tflite-runtime-edgetpu")

def info_missing():
if missing:
print("Optional packages missing:", ", ".join(missing))
print("Install suggestions: pip install onnx onnxruntime onnxruntime-tools onnx-tf tensorflow numpy pillow opencv-python pycoral tflite-runtime")

def load_images_from_dir(d, size, max_images=None):
imgs = []
files = sorted(glob.glob(os.path.join(d, "*.*")))
for f in files[:max_images]:
try:
im = Image.open(f).convert("RGB").resize(size, Image.BILINEAR)
arr = np.asarray(im).astype(np.float32)
imgs.append(arr)
except Exception:
continue
return imgs

def infer_onnx_session(session, inputs, input_name):
out = session.run(None, {input_name: inputs})
return out

def top1_accuracy(preds, labels):
if labels is None: return None
correct = 0
for p, l in zip(preds, labels):
if int(np.argmax(p)) == int(l): correct += 1
return correct / len(labels)

def representative_gen(imgs, input_name):
for im in imgs:
yield {input_name: np.expand_dims(im.astype(np.float32), 0)}

def main():
parser = argparse.ArgumentParser()
parser.add_argument("--onnx", required=True)
parser.add_argument("--data_dir", required=True)
parser.add_argument("--labels", default=None)
parser.add_argument("--batch_size", type=int, default=1)
parser.add_argument("--num_calib", type=int, default=100)
parser.add_argument("--edgetpu_compile", action="store_true")
parser.add_argument("--device", choices=["cpu","edgetpu"], default="cpu")
args = parser.parse_args()
info_missing()

model_path = args.onnx
if not os.path.exists(model_path):
print("ONNX model not found:", model_path); return

# Load ONNX to inspect input size
if onnx:
m = onnx.load(model_path)
gi = m.graph.input[0].type.tensor_type.shape.dim
try:
h = int(gi[2].dim_value); w = int(gi[3].dim_value)
except Exception:
h,w = 224,224
else:
h,w = 224,224

# Prepare calibration images
imgs = load_images_from_dir(args.data_dir, (w,h), max_images=args.num_calib)
if not imgs:
print("No images found in data_dir"); return
labels = None
if args.labels and os.path.exists(args.labels):
labels = [int(x.strip()) for x in open(args.labels).read().splitlines()]

# ONNX quantization
quant_model = Path("model_int8.onnx")
quant_method = "skipped"
try:
if quantize_static and ort:
class SimpleReader(CalibrationDataReader):
def __init__(self, imgs, name):
self.data = imgs; self.name = name; self.idx = 0
def get_next(self):
if self.idx >= len(self.data): return None
v = {self.name: np.expand_dims(self.data[self.idx].astype(np.float32),0)}
self.idx += 1
return v
sess = ort.InferenceSession(model_path, providers=["CPUExecutionProvider"])
input_name = sess.get_inputs()[0].name
reader = SimpleReader(imgs, input_name)
quantize_static(model_path, str(quant_model), reader)
quant_method = "static"
elif quantize_dynamic:
quantize_dynamic(model_path, str(quant_model))
quant_method = "dynamic"
except Exception as e:
print("Quantization failed:", e); quant_model = Path(model_path); quant_method = "none"

# ONNX Runtime CPU inference
cpu_results = {}
if ort:
sess = ort.InferenceSession(str(quant_model), providers=["CPUExecutionProvider"])
input_name = sess.get_inputs()[0].name
warm = 5
for _ in range(warm):
infer_onnx_session(sess, np.expand_dims(imgs[0].astype(np.float32),0), input_name)
times = []
preds = []
for im in imgs:
t0 = time.time()
out = infer_onnx_session(sess, np.expand_dims(im.astype(np.float32),0), input_name)
times.append((time.time()-t0)*1000)
preds.append(out[0][0])
cpu_results = {"latency_ms": sum(times)/len(times), "throughput":1000.0/(sum(times)/len(times)), "top1": top1_accuracy(preds, labels), "quant": quant_method}

# ONNX -> TF -> TFLite INT8
tflite_path = Path("model_int8.tflite")
edgetpu_compiled = False
if onnx_tf_prepare and tf:
try:
tf_rep = onnx_tf_prepare(onnx.load(model_path))
saved = "tmp_saved_model"
tf_rep.export_graph(saved)
converter = tf.lite.TFLiteConverter.from_saved_model(saved)
def rep_gen():
for im in imgs[:args.num_calib]:
yield [np.expand_dims(im.astype(np.float32),0)]
converter.optimizations = [tf.lite.Optimize.DEFAULT]
converter.representative_dataset = lambda: (x for x in (np.expand_dims(im.astype(np.float32),0) for im in imgs[:args.num_calib]))
converter.target_spec.supported_ops = [tf.lite.OpsSet.TFLITE_BUILTINS_INT8]
converter.inference_input_type = tf.uint8
converter.inference_output_type = tf.uint8
tflite_model = converter.convert()
tflite_path.write_bytes(tflite_model)
except Exception as e:
print("TFLite conversion skipped:", e)

# EdgeTPU compile
if args.edgetpu_compile and tflite_path.exists():
if os.system("which edgetpu_compiler > /dev/null 2>&1") == 0:
print("Running edgetpu_compiler...")
rc = os.system(f"edgetpu_compiler {tflite_path} -o .")
edgetpu_compiled = (rc == 0)
else:
print("edgetpu_compiler not found on PATH; install from Coral site")

# Coral inference
edgetpu_results = {}
if args.device == "edgetpu" and make_interpreter and tflite_path.exists():
try:
compiled = next(Path(".").glob("*.tflite")) # compiled name heuristic
interp = make_interpreter(str(compiled))
interp.allocate_tensors()
input_details = common.input_details(interp)
warm = 5
for _ in range(warm):
common.set_input(interp, np.expand_dims(imgs[0].astype(np.uint8),0))
interp.invoke()
times=[]; preds=[]
for im in imgs:
common.set_input(interp, np.expand_dims(im.astype(np.uint8),0))
t0=time.time(); interp.invoke(); times.append((time.time()-t0)*1000)
out = classify.get_classes(interp, top_k=1)
preds.append(np.eye(1000)[out[0].id] if out else np.zeros(1000))
edgetpu_results = {"latency_ms": sum(times)/len(times), "throughput":1000.0/(sum(times)/len(times)), "top1": top1_accuracy(preds, labels)}
except Exception as e:
print("Coral inference skipped:", e)

# Report
print("\nSummary")
print(f"ONNX model: {model_path}")
print(f"Quantized ONNX: {quant_model} method={quant_method}")
if cpu_results:
print(f"CPU latency_ms={cpu_results['latency_ms']:.2f} throughput={cpu_results['throughput']:.2f} top1={cpu_results['top1']}")
if edgetpu_results:
print(f"EdgeTPU latency_ms={edgetpu_results['latency_ms']:.2f} throughput={edgetpu_results['throughput']:.2f} top1={edgetpu_results['top1']}")
print("Done")

if __name__ == "__main__":
main()

// (c)RS

*

Brain Depth:

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/2026/01/inferencing.html

https://science.n-helix.com/2023/06/tops.html

Training Networks:

https://science.n-helix.com/2023/06/tops.html
https://science.n-helix.com/2023/06/map.html
https://science.n-helix.com/2022/08/jit-dongle.html
https://science.n-helix.com/2022/06/jit-compiler.html

https://science.n-helix.com/2023/02/pm-qos.html
https://science.n-helix.com/2023/06/ptp.html

*****

about:gpu

While we are not supporting 420, Let's Support 422! Rupert S @ Chrome dev

YVU_420: not supported, YUV_420_BIPLANAR: not supported, YUVA_420_TRIPLANAR: not supported

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

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

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

https://drive.google.com/file/d/10P7AzvY2RNF3FSPVhkGDgILamsIdoTVM/

code : https://filebin.net/5gz2eswycm9nl963/FRC%20Upscaling%20with%20code%202025.txt

https://filebin.net/5gz2eswycm9nl963/Upscaling%20Colour%20strategy%20-%20With%20Proof%20-%20RS%202025.txt

https://filebin.net/sog7knhxc5tuxbfe/Directory-Sort-RS.zip