DREAMPlace
DALI
DREAMPlace | DALI | |
---|---|---|
2 | 5 | |
624 | 4,931 | |
- | 1.4% | |
7.4 | 9.6 | |
29 days ago | 6 days ago | |
C++ | C++ | |
BSD 3-clause "New" or "Revised" License | Apache License 2.0 |
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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.
DALI
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[D] Will data augmentations work faster on TPUs?
Another option is DALI https://github.com/NVIDIA/DALI For my project while training EfficientNet2, it was a game changer. But it a way harder to implement in code than TorchVision or Kornia.
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DirectStorage - Loading data to GPU *directly* from the SSD drive, almost without using CPU
Check out https://github.com/nvidia/DALI
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mmap_ninja: Speedup your training dramatically by using memory-mapped files for your dataset
Small question if you are using GPU: How to this compare to GPUDirect Storage from Nvidia? can you have even more speedup by using both? I never toy with it, but the DALI project from Nvidia seem to tackle the same data loading problem.
- [D] Efficiently loading videos in PyTorch without extracting frames
What are some alternatives?
tensorRT_Pro - C++ library based on tensorrt integration
Pytorch - Tensors and Dynamic neural networks in Python with strong GPU acceleration