DREAMPlace VS deepdetect

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

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.

For those seeking a lightweight solution for setting up deep learning REST APIs across platforms without the complexity of Kubernetes, Deepdetect is worth considering.

But you need to have good reasons to do it. Ours is that we have a multi-backend framework, and that we don't want any step in between dev & run. C++ allows for this since the same code can run on training server and edge device as needed. It also allows for building full AI applicatioms with great performances (e g. real time) We dev & use https://github.com/jolibrain/deepdetect for these purposes and it serves us very well, but it's not the faint of heart !

- Real-time is achieved through our full C++ Open Source backend DeepDetect, https://github.com/jolibrain/deepdetect. We use CUDA along with OpenCV and TensorRT to chain multiple models (ring detection and generator mostly), and we make sure the data remain within CUDA memory at all time. This allows us to reach ~60 FPS on 1080Ti and 20% more on average on an RTX3090.

Interesting, thanks. Recently benchmarked inference with Vulkan/MoltenVK/NCNN, M1 GPU is roughly 30% faster than M1 CPU, https://github.com/jolibrain/deepdetect/pull/1105 for single batch inference (NCNN does not really support batch size > 1).