Wednesday, October 19, 2022

Machine Learning Equates Solve Table for Advanced ML

Machine Learning Equates Solve Table for Advanced ML (c)RS

ML & Code Efficiency Heuristic Search,
Python & of course all runtimes of GPU & CPU Firmware & Logical thought,

Apologies for not expressly stating all {Mul+ & all} Accumulator strategies, these are hard to work out! But basic edge detection is a SiMD Example RS


Core Motivations of ML

ML Learning is a branch of artificial intelligence that focuses on using data and algorithms to imitate the way that humans learn & improving ML Method accuracy.

ML Learning can be applied to various domains, such as image processing, natural language processing, speech recognition & code optimization.

ML Learning can use different techniques; Such as supervised learning, unsupervised learning & reinforcement learning, depending on the type and availability of data.

Some of the common techniques used in ML Learning are:

Edge detection: a process of identifying the boundaries of objects in images or videos.

Accent recognition: a process of identifying the regional or social variation of speech.

Language processing: a process of analyzing and generating natural language texts.

Code optimization: a process of improving the performance or quality of code by using various methods, Such as compilers, libraries, or heuristics.

The Objective is to improve both ML & Minds.


I think that considering the stated philosophy, There is more room for education on social conduct.


Int8:SiMD : Maths & Logic

This is about how you think about components such as INT8, INT4(Xbox) & SiMD, You have to classify by necessity & optimise the structure.

You can shape the game reality with specific control objects & statics!
Maths in SiMD & Int8 & Machine Learning in Int8 & SiMD; SiMD is hard maths, Int8 is soft edge inference...

Both are maths; But soft logic is not a PROOF Math but can be proof; Hard math is not 'Invention & Imagination 'Exactly''

But we have both to improve performance.


Solve Table of Statistically provable Machine Equates & Solves : Table of function competitors & Operators.

"I know this is depressing from my end with a FX8320E with AVX but if you multi tune the CPU Kernel for the RX / RTX that 512DL AVX would have meaning, If you are kind you will allow machine learning on the AVX FX8320E Level to work on SiMD Yes / No comparisons !"

#ML Learning: This explains why we teach kids art & reading first! But maths is quickly next, 
Because all else is pointless; That we do not learn with logic & Teach with logic.

Here is how to create a better mind #ML
Train your eyes with art on the concepts of edges, curves, Colours & Shading and love,
Educate your minds; Learn today & be quite aware how clever & sharp you will be.

Humain Operations

Edge Detection
Such as teaching your child edge detect in art ;)

Smooth & Blend & Sharpen,
All interpretive

Accent Recognitions & Language

Interpret as follows


Heuristic Code optimise

When it comes to sorting methods, We Identify common techniques..
For example frequently used technologies such as:

Audio & Visual information

Primarily we identify common optimisations; Compilers have libraries of them!

Audio & Video Encoded data use Wavelet Images, We can ResNet Them & also Edge Detect & Gaussian Detect contrast, Colour, Shape

Language is an uncommon syntax, But we have audio commons & Accent identification is also potentially Audio Context.

Code context is Logic, Function, Utility, Design, Motive



SiMD Performance : RS

Performance per WATT of MMX & MMX+ & SSE & AVX Machine Learning & Shader code; Is a matter of 8x8Bit & 16x16Bit Code on GPU

Our role is to reduce complex un-cache-able ML to Cache Enabled 64KB
Modelling of 1990's without Quality loss of 32Bit++ 64Bit+

8x8Bit sharpening MMX Becomes Dual Pipe (16x16bit)*2 in 32Bit Dual 16 Pipeline & Twice as sharp
Machine Learning method for MMX Is Fast & Cheap, MMX2 More Compatible,
Intrinsic improvements such as combined ops & DOT4 Further improve the performance of under 1MB Code..

Performance & Function per WATT, Is unbeaten; Let us prove it!

For example Quake has MMX Emulation & MMX Dithering code on 3D Textures,
In 8Bit 256 Colours dithering is noticeable; In 15Bit to 32Bit the small shade difference in dithering colour is subtle & flawless,
Improving light subtilty & Colour pallet WCG & HDR 10Bit to 16Bit per channel.

SiMD & Int8 & dp4a & F16/F32/F64>:

The way SiMD Repeating Parallel batches of instruction can still side load data,
Data is loaded into the 'calculation set',_multiple_data

SiMD Consist of 8Bit to 64Bit Long & Floats,
SiMD are simple instructions; Or so they think; SiMD are relatively complex instructions..
For example 4/1 of a page full of arithmetic code; However our goal is to use Heuristics & logic to circumvent the Artifacts/Errors in self generated code,

In addition to using problem solving tables to choose instructions that advantage our analysis (Machine Learning),
We also can choose the most probably optimal code type.

Our outset objective is to decide if we want to use CPU Feature types:


Depending on the Mathematical Qualities of each ML Node & the questions they are asking,
For examples:

A simple ResNet Image identification uses edge detect & for that we need for example SiMD Matrix Edge Detection

Speech requires identifying Words in a codec, So obviously we need a Decoder & Encoder,
Word identifiers & correctness checking; But firstly we need to identify accent to correctly choose words..

We also need to classify words by Idea grouping (DataBase, Open Database)

As you can see; We will be defining many of these function groups as SiMD & Float,
Effective use of Int8 differentiation, Comparators & Maths operations has many benefits; So does JIT Compile.



Solve Table of Statistically provable Machine Equates & Solves : Table of function competitors & Operators.

Runtime Library - Multiple Solve Table

I would like a Solve Table of Statistically provable Machine Equates & Solves that make the equivalent of Maths Compilers such as RUST & Fortran's

For example basic ML code test function loops are basically compatible with X-OR Comparators on AVX! Other functions such as greater or less than; Are AVX Compatible.

Machine Learning : List of actions that are SiMD Baseline: Statistical Observance and Solve Tables

Yes or no comparator X-OR
Memory array Byte Swap
Greater or less than with swap or with X-OR Roll
Memory save & store
Edge comparisons
Compares (Colour, Math, Equate, Target, Solve if)

There are more! Statistical Observance and Solve Tables.

Examples 2:

Shape compare is a matter of inner & outer Vector : Comparison & X-OR, Larger outside & X-OR The differentiation:
By Dot,
By Mass (non literal dot difference comparator by axis),
Actual Mass
Density : Lumina, Weight, Mole, Mass / Area

Edge Solve : X-OR ~= Colour, Lumina, Shade, Vibrancy, Distance, Matrix Solve 3D>=2D Flattened Comparator
If = X-OR=N<0.0001 Then Compare &= Mutex Solve / Average

Polygon Join/Merge Tessellation : If Model = Same (T1 + T2 If (T1 + T2)/2 = Difference Less Than 0.0001 | = Merge/Converge


Audio, Video & High precision Float ML

tensors & full onnx configuration : Upscaling : While we are not sure how much ML we need & at what precision,

We can be sure that 32Bit (per channel) Value RGBA (Multiple layer) requires at least 8Bit to 16Bit per channel final precision; So here is a list:

Required Value of output, Neural Network precision guide table: RS

8Bit, 10Bit, 12Bit, 16Bit

Input network precision average bit retention (for RAM some error is allowed)
6Bit, 8Bit, 10Bit, 14Bit, 16Bit

Classifiers as we know can be,
Int 2Bit 4Bit, 8Bit, 16Bit, 32Bit
2 Bit is unlikely & 32Bit is for Dream Smooth 16Bit+ Precision output

Output Float (Mostly FP & F16b)
16Bit = { 8Bit, 10Bit, 12Bit }
24Bit, 32Bit, 64Bit = { 16Bit, 32Bit, 48Bit }
We can upscale : Audio, Video, Content & Polygons, We classify Quality by expectations & Quantify by percent %

Rupert S


FPGA BitFile & Code Opt (c)RS 2021-01

In my view heuristics in compilers are a choice for those who do not wish to include direct ML compiled into their code,
This is understandable in terms of terminator & cylons & indeed flawed beings or even good ones with depression!

However the application of branch optimisation is a sample code optimisation that can 'Plug In' to branch caching on the CPU & GPU.

Heuristics are not just code in the compiler; They are also micro code selecting a probable branch; Although code that forces a branch can be flawed..

Both heuristics, Branch probability selection & ML can run in parts of the code to select probable path!

Yes fundamentally any code that modifies behaviour is a catch bullet frame for not sound 'Fortrans code is rock solid' Rust is also supposed to be solid.

Including soundly made heuristic code & branch probability code ML in your inline routines; 'Very much interpretive master jedi'; But it can be done!

Question is How big? & how fixed?

25KB per 3MB on average?

ML & Heuristics like my application FPGA BitFile & Code Opt (c)RS 2021-01

can be applied at runtime & remain only for selecting the fastest path or the best; In terms of which Processor function to run code for.

(c)Rupert S


TOPCloud Scaled Flexible WebASM & WebGPU & MathML!

Quite flexible for use on Monitors & TV's; Light processor load on simple tasks & offloadable such as TOPCloud!

You may be thinking Offloading is impracticable because that requires one of two things:

JIT Compiler Dongle..
USB device such as Firestick or GPU & CPU (With OpenCL Compat)

Server! so internet & service provision!
Impossible? No; WebAdvert supported TV's need both!
So why not HPC TOPCloud? could make a HOT TV a lot cooler & Eco friendly with Server repeating tasks:

Quality Service
Service availability

TOPCloud Offload Logic:

In terms of WebASM & WebGPU & MathML; TOPCloud provides sufficient advantages to be considered a core utility..

While Offloading repeating content such as Siteload core stack (Server) & Localising configuration such as Webpage size & DPI & Dynamic font arrangements that require thought.

In terms of Offloaded function & Efficient system load for large configurations..

Especially efficient configurations such as TPU, Coral, GPU work & Cloud CPU that have large optimised stacks & installed drivers.



Scaling; We can classify by colour or creativity. (c)RS

If you use TOPCloud, you can share between different displays in the TOP's Sense..
but mostly you would need cloud presence,

Mostly this would be about making the most out of TOP heavy Business GPU & personal ones in your computer or consoles.

But sharing common tasks such as scaling movies by type or by identifying a single movie to upscale...

Now you might be asking what we would be doing there?
Well a single movie uses the same materials in our ML; We can analyse the class & optimise the scaling by class..

For those familiar with games & FSR; We familiarise our code with a single game!
By doing this we improve our product and can therefore classify by:

Type, FPS for example & RTS

We can classify by colour or creativity...

We do not simply have to roll the dice on General Scaling, We can use classifiers:

Frame Rate
Colour & Composure

Rupert S

PoCL Source & Code


We all think our own way; Potential is always there on a Runtime Library - Multiple Solve Table

Machine learning | Equate ~= Multi Layer Wavelet Abstraction

(c)Rupert S 2022-10

This one will suite Dedicated ARM Machine in body armour 'mental state' ARM Router & TV
(ARM Learning 4K ROM; Safe Larger USB ROM)

Android & Linux ARM Processor configurations; routers & TV's upgrade files, Update & improve


Python Deep Learning: configurations

AndroLinuxML :

Linux :

Windows :

*Windows {
To Compress using CPU/GPU: MS-OpenCL

Upscale DL

Machine Learning SDK's,
You may not have a Machine Learning SDK to accelerate your GPU/CPU/Device

3 main ones, but Python does not guarantee an accelerator!
Obviously Python Builds with Accelerators work!

HW Build Source : Upscale DL

PoCL Source & Code


Neural Engine

ML List & Services

Machine learning | Equate ~= Multi Layer Wavelet Abstraction

(documents) JIT & OpenCL & Codec :

Include vector today *important* RS

Eclectic & for the codecs of the world! OVCCANS (install and maintain as provided HPC Pack)


Transversal processing availability : Transparent Task Sharing Protocols

Machine Learning

Innate Compression, Decompression

Best NPM site on world

(Simple Install) Website Cache JS Updated 2021-11 (c)RS
(Simple Install) Science & Research Node High Performance Computing
Linux & Android

Presenting JIT for hardware interoperability & function :

(Simple Install) Website Server Cache JS Updated 2021-11 (c)RS
(Simple Install) Website Server Cache JS Work Files Zip Updated
2021-11 (c)RS


Ideal for 4Bit Int4 XBox & Int8 GPU
PULP-NN: accelerating quantized neural networks on parallel ultra-low-power RISC-V processors - Bus-width 8-bit, 4-bit, 2-bit and 1-bit

ML Proof case SVM (Multi-Dimensional-Elliptic,98%) aDaBoost M1(Mac,91%) - COVID-19 Prediction Using Supervised Machine Learning - Irfan_Ali_MEng_2023



SiMD Gaussian Blending & Dithering - Better_Fixed_Point_Filtering_with_Averaging_Trees

Vectorization of Kernel and Image Subsampling in FIR Image Filtering

Implementation of a High-Quality Dolby Digital Decoder Using SiMD MMX™ Technology


Common techniques used in ML Learning are edge detection, accent recognition, language processing, and code optimization.

Basic ML Feature list; Also for learning

Edge detection is a process of identifying the boundaries of objects in images or videos.

Accent recognition is a process of identifying the regional or social variation of speech.

Language processing is a process of analyzing and generating natural language texts.

Code optimization is a process of improving the performance or quality of code.

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