Machine Learning Equates Solve Table for Advanced ML (c)RS
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.
RS
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Solve Table of Statistically provable Machine Equates & Solves : Table of function competitors & Operators.
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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!
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.
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.
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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
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We all think our own way; Potential is always there on a Runtime Library - Multiple Solve Table
Machine learning | Equate ~= Multi Layer Wavelet Abstraction
https://science.n-helix.com/2022/09/ovccans.html
https://www.youtube.com/watch?v=-9lCpfrOQQ4
(c)Rupert S 2022-10
https://is.gd/LEDSource
https://is.gd/BTSource
https://science.n-helix.com/2021/03/brain-bit-precision-int32-fp32-int16.html
https://science.n-helix.com/2022/08/jit-dongle.html
https://science.n-helix.com/2022/06/jit-compiler.html
https://is.gd/MLCodecShaping
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This one will suite Dedicated ARM Machine in body armour 'mental state' ARM Router & TV
(ARM Learning 4K ROM; Safe Larger USB ROM) https://bit.ly/3Afn1Y4
https://drive.google.com/file/d/102pycYOFpkD1Vqj_N910vennxxIzFh_f/view?usp=sharing
Android & Linux ARM Processor configurations; routers & TV's upgrade files, Update & improve
https://drive.google.com/file/d/1JV7PaTPUmikzqgMIfNRXr4UkF2X9iZoq/
Providence: https://www.virustotal.com/gui/file/0c999ccda99be1c9535ad72c38dc1947d014966e699d7a259c67f4df56ec4b92/
https://www.virustotal.com/gui/file/ff97d7da6a89d39f7c6c3711e0271f282127c75174977439a33d44a03d4d6c8e/
Python Deep Learning: configurations
AndroLinuxML : https://drive.google.com/file/d/1N92h-nHnzO5Vfq1rcJhkF952aZ1PPZGB/view?usp=sharing
Linux : https://drive.google.com/file/d/1u64mj6vqWwq3hLfgt0rHis1Bvdx_o3vL/view?usp=sharing
Windows : https://drive.google.com/file/d/1dVJHPx9kdXxCg5272fPvnpgY8UtIq57p/view?usp=sharing
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Machine learning | Equate ~= Multi Layer Wavelet Abstraction
This one will suite Dedicated ARM Machine in body armour 'mental state' ARM Router & TV
(ARM Learning 4K ROM; Safe Larger USB ROM) https://bit.ly/3Afn1Y4
https://drive.google.com/file/d/102pycYOFpkD1Vqj_N910vennxxIzFh_f/view?usp=sharing
Android & Linux ARM Processor configurations; routers & TV's upgrade files, Update & improve
https://drive.google.com/file/d/1JV7PaTPUmikzqgMIfNRXr4UkF2X9iZoq/
Providence: https://www.virustotal.com/gui/file/0c999ccda99be1c9535ad72c38dc1947d014966e699d7a259c67f4df56ec4b92/
https://www.virustotal.com/gui/file/ff97d7da6a89d39f7c6c3711e0271f282127c75174977439a33d44a03d4d6c8e/
Python Deep Learning: configurations
AndroLinuxML : https://drive.google.com/file/d/1N92h-nHnzO5Vfq1rcJhkF952aZ1PPZGB/view?usp=sharing
Linux : https://drive.google.com/file/d/1u64mj6vqWwq3hLfgt0rHis1Bvdx_o3vL/view?usp=sharing
Windows : https://drive.google.com/file/d/1dVJHPx9kdXxCg5272fPvnpgY8UtIq57p/view?usp=sharing
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Machine learning | Equate ~= Multi Layer Wavelet Abstraction
https://science.n-helix.com/2022/09/ovccans.html
https://science.n-helix.com/2023/02/smart-compression.html
https://science.n-helix.com/2021/10/he-aacsbc-overlapping-wave-domains.html
https://science.n-helix.com/2023/02/smart-compression.html
https://science.n-helix.com/2021/10/he-aacsbc-overlapping-wave-domains.html
(documents) JIT & OpenCL & Codec : https://is.gd/DisplaySourceCode
Include vector today *important* RS https://vesa.org/vesa-display-compression-codecs/
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/2022/04/vecsr.html
https://science.n-helix.com/2016/04/3d-desktop-virtualization.html
https://science.n-helix.com/2019/06/vulkan-stack.html
https://science.n-helix.com/2019/06/kernel.html
https://science.n-helix.com/2022/03/fsr-focal-length.html
https://science.n-helix.com/2018/01/integer-floats-with-remainder-theory.html
https://science.n-helix.com/2022/08/simd.html
Eclectic & for the codecs of the world! OVCCANS (install and maintain as provided HPC Pack)
https://science.n-helix.com/2018/09/hpc-pack-install-guide.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/2022/04/vecsr.html
https://science.n-helix.com/2016/04/3d-desktop-virtualization.html
https://science.n-helix.com/2019/06/vulkan-stack.html
https://science.n-helix.com/2019/06/kernel.html
https://science.n-helix.com/2022/03/fsr-focal-length.html
https://science.n-helix.com/2018/01/integer-floats-with-remainder-theory.html
https://science.n-helix.com/2022/08/simd.html
Eclectic & for the codecs of the world! OVCCANS (install and maintain as provided HPC Pack)
https://science.n-helix.com/2018/09/hpc-pack-install-guide.html
*****
Best NPM site on world https://npm.n-helix.com/bundles/
(Simple Install) Website Cache JS Updated 2021-11 (c)RS https://bit.ly/CacheJS
(Simple Install) Science & Research Node High Performance Computing
Linux & Android https://is.gd/LinuxHPCNode
Presenting JIT for hardware interoperability & function :
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