XNNPACK
Genann
XNNPACK | Genann | |
---|---|---|
8 | 7 | |
1,700 | 1,905 | |
1.6% | - | |
9.9 | 0.0 | |
6 days ago | 8 months ago | |
C | C | |
GNU General Public License v3.0 or later | zlib License |
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XNNPACK
- Xnnpack: High-efficiency floating-point neural network inference operators
- Can a NPU be used for vectors?
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Performance critical ML: How viable is Rust as an alternative to C++
Why are you writing your own inference code in C++ or Rust instead of using some kind of established framework like XNNPACK?
- [P] Pure C/C++ port of OpenAI's Whisper
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[Discussion] Is XNNPACK a part of mediapipe? or should be additionally configured with mediapipe?
XNNPACK - https://github.com/google/XNNPACK
- WebAssembly Techniques to Speed Up Matrix Multiplication by 120x
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Prediction: Macs won't see many new games, no matter how powerful their hardware is
Ok, concrete example time! At work, we're going to be using some software which includes XNNPACK, which is a library of highly-optimised operations for doing neural-network inference. This is the sort of thing where people have gone in and specifically tuned for performance, and nope, there's no attempt at all made to have code which is different for Intel/AMD or Apple/Other ARM. What they target is elements of the ISA, like NEON (i.e. ARM SIMD) and SSE, AVX etc. on x86(-64). And Wasm SIMD for Wasm.
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Where are Nvidia's DLSS models stored and how big are they?
It's quite simple. https://github.com/google/XNNPACK for example.
Genann
- Simple neural network library in ANSI C
- Genann: Simple neural network library in ANSI C
- Machine learning Library in C?
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Ask HN: What ML platform are you using?
> I am very much a beginner in the space of machine learning
While the (precious and useful) advice around seem to cover mostly the bigger infrastructures, please note that
you can effectively do an important slice of machine learning work (study, personal research) with just a battery-efficiency-level CPU (not GPU), in the order of minutes, on a battery. That comes before going to "Big Data".
And there are lightweight tools: I am current enamoured with Genann («minimal, well-tested open-source library implementing feedfordward artificial neural networks (ANN) in C»), a single C file of 400 lines compiling to a 40kb object, yet well sufficient to solve a number of the problems you may meet.
https://codeplea.com/genann // https://github.com/codeplea/genann
After all, is it a good idea to have tools that automate process optimization while you are learning the deal? Only partially. You should build - in general and even metaphorically - the legitimacy of your Python ops on a good C ground.
And: note that you can also build ANNs in R (and other math or stats environments). If needed or comfortable...
Also note - reminder - that the MIT lessons of Prof. Patrick Winston for the Artificial Intelligence course (classical AI with a few lessons on ANNs) are freely available. That covers the grounds relative to climb into the newer techniques.
- Small tensor library in C99
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C Deep
Genann - Simple ANN in C89, without additional dependencies. Zlib
What are some alternatives?
ncnn - ncnn is a high-performance neural network inference framework optimized for the mobile platform
tiny-cnn - header only, dependency-free deep learning framework in C++14
gemm-benchmark - Simple [sd]gemm benchmark, similar to ACES dgemm
Recast/Detour - Industry-standard navigation-mesh toolset for games
cpuid2cpuflags - Tool to generate CPU_FLAGS_* for your CPU
frugally-deep - Header-only library for using Keras (TensorFlow) models in C++.
wasmblr - C++ WebAssembly assembler in a single header file
tensorflow - An Open Source Machine Learning Framework for Everyone
ruby-fann - Ruby library for interfacing with FANN (Fast Artificial Neural Network)
ANNetGPGPU - A GPU (CUDA) based Artificial Neural Network library
HIP-CPU - An implementation of HIP that works on CPUs, across OSes.
BayesOpt - BayesOpt: A toolbox for bayesian optimization, experimental design and stochastic bandits.