Artificial intelligence is the common underlying theme for most of this week’s updates. Among them, Nvidia is in the news with an EDA research work, after last week announcement concerning its solution for computational lithography – the last software step before mask production.
Nvidia research on AI-based macro placement
At the recent ISPD (International Symposium on Physical Design), a group of Nvidia researchers presented a paper on AI-based macro placement. The paper proposes AutoDMP, a methodology that leverages DREAMPlace, a preexisting open-source GPU-accelerated placer, to place macros and standard cells concurrently in conjunction with automated parameter tuning using a multi-objective hyperparameter optimization technique. As a result, the team could generate high-quality predictable solutions, improving the macro placement quality of academic benchmarks compared to baseline results generated from academic and commercial tools. According to the Nvidia researchers, AutoDMP is also computationally efficient, optimizing a design with 2.7 million cells and 320 macros in three hours on a single Nvidia DGX Station A100. The key contributions of the work include using multi-objective Bayesian optimization to search the design space of macro placements, targeting three PPA proxy objectives post-place: wirelength, cell density, and congestion; using a two-level PPA evaluation scheme to manage the complexity of the search space; and enhancing the DREAMPlace placer. Open-source benchmarks used include Ariane, a single core Risc-V CPU; the MemPool Group and BlackParrot designs, many-core Risc-V CPUs with large amounts of on-chip SRAMs; and an NVDLA partition.
A previous research work on AI-based macro placement, from Google, had been criticized for not providing enough publicly available data and for comparing the AI performance to an unspecified human expert’s performance. The new Nvidia work seems to be able to withstand these types of criticism, as it includes details on benchmarking and compares the AI performance with a commercial EDA tool, Cadence Innovus. The work’s source code is released on GitHub.
