DREAMPlace
tensorRT_Pro
DREAMPlace | tensorRT_Pro | |
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2 | 1 | |
624 | 2,400 | |
- | - | |
7.4 | 3.1 | |
25 days ago | 12 months ago | |
C++ | C++ | |
BSD 3-clause "New" or "Revised" License | MIT License |
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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.
tensorRT_Pro
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