tiny-cuda-nn
DREAMPlace
tiny-cuda-nn | DREAMPlace | |
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9 | 2 | |
3,418 | 622 | |
2.4% | - | |
5.9 | 7.4 | |
about 1 month ago | 14 days ago | |
C++ | C++ | |
GNU General Public License v3.0 or later | BSD 3-clause "New" or "Revised" License |
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tiny-cuda-nn
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[D] Have their been any attempts to create a programming language specifically for machine learning?
In the opposite direction from your question is a very interesting project, TinyNN all implemented as close to the metal as possible and very fast: https://github.com/NVlabs/tiny-cuda-nn
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A CUDA-free instant NGP renderer written entirely in Python: Support real-time rendering and camera interaction and consume less than 1GB of VRAM
This repo only implemented the rendering part of the NGP but is more simple and has a lesser amount of code compared to the original (Instant-NGP and tiny-cuda-nn).
- Tiny CUDA Neural Networks: fast C++/CUDA neural network framework
- Making 3D holograms this weekend with the very “Instant” Neural Graphics Primitives by nvidia — made this volume from 100 photos taken with an old iPhone 7 Plus
- NVlabs/tiny-CUDA-nn: fast C++/CUDA neural network framework
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Small Neural networks in Julia 5x faster than PyTorch
...a C++ library with a CUDA backend. But these high-performance building blocks might only be saturating the GPU fully if the data is large enough.
I haven't looked at implementing these things, but I imagine uf you have smaller networks and thus less data, the large building blocks may not be optimal. You may for example want to fuse some operations to reduce memory latency from repeated memory access.
In PyTorch world, there are approaches for small networks as well, there is https://github.com/NVlabs/tiny-cuda-nn - as far as I understand from the first link in the README, it makes clever use of the CUDA shared memory, which can hold all the weights of a tiny network (but not larger ones).
- [R] Instant Neural Graphics Primitives with a Multiresolution Hash Encoding (Training a NeRF takes 5 seconds!)
- Tiny CUDA Neural Networks
- Real-Time Neural Radiance Caching for Path Tracing
DREAMPlace
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A Simulated Annealing FPGA Placer in Rust
Yes, see "DREAMPlace: DREAMPlace: Deep Learning Toolkit-Enabled GPU Acceleration for Modern VLSI Placement".[1] The technique in particular rather reformulates VLSI placement in terms of a non-linear optimization problem. Which is how ML frameworks (broadly) work, optimizing approximations to high-dimensional non-linear functions. So it's not like, shoving the netlist it into an LLM or an existing network or anything.
Note that DREAMPlace is a global placer; it also comes with a detail placer but global placement is what it is targeted at. I don't know of an appropriate research analogue for the routing phase of the problem that follows placing, but maybe someone else does.
[1] https://github.com/limbo018/DREAMPlace
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Nvidia: GPUs can do better chip design in a few days than 10 man year
Huge part of why OpenROAD (and as this article.indicates, nvidia) are so focused on machine learning! Because the nitty gritty of chip design has abundant gnarly problems requiring deep deep expertise. Deploying software engineers is hard. But building ml is kind of our bag!
There's another nice upstart opensource project with even fancier ml placememt systems that spawned recently out of the openroad world, dreamplace, https://github.com/limbo018/DREAMPlace
This is just gonna get more & more biased against a couple super smart engineers who we've deeply entrusted to divine inner the workings of the chips on, & become increasingly a set of better modelled problems that we can machine learningly optimize.
What are some alternatives?
instant-ngp - Instant neural graphics primitives: lightning fast NeRF and more
tensorRT_Pro - C++ library based on tensorrt integration
blis - BLAS-like Library Instantiation Software Framework
DALI - A GPU-accelerated library containing highly optimized building blocks and an execution engine for data processing to accelerate deep learning training and inference applications.
diffrax - Numerical differential equation solvers in JAX. Autodifferentiable and GPU-capable. https://docs.kidger.site/diffrax/
deepdetect - Deep Learning API and Server in C++14 support for Caffe, PyTorch,TensorRT, Dlib, NCNN, Tensorflow, XGBoost and TSNE
juliaup - Julia installer and version multiplexer
onnxruntime - ONNX Runtime: cross-platform, high performance ML inferencing and training accelerator
RecursiveFactorization
Cores-VeeR-EH1 - VeeR EH1 core
RecursiveFactorization.jl
ncnn - ncnn is a high-performance neural network inference framework optimized for the mobile platform