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Arxiv
3d
51
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RocketPPA: Code-Level Power, Performance, and Area Prediction via LLM and Mixture of Experts
- RocketPPA is a new tool that predicts power, performance, and area (PPA) metrics directly at the code-level abstraction using HDL code as input.
- It utilizes an LLM-based regression model that integrates a large language model (LLM) with a mixture-of-experts (MoE) architecture composed of multilayer perceptrons (MLPs).
- The LLM interprets the input HDL code and uses its final hidden-layer representations to predict PPA metrics. Low-rank adaptation (LoRA) enables efficient LLM training.
- RocketPPA includes an LLM-based HDL code repair framework to generate a synthesizable training dataset.
- Experimental results show that RocketPPA significantly improves accuracy in PPA estimation compared to previous methods like Llama3-MetRex-8B.
- At a 10% relative error threshold, RocketPPA enhances area prediction pass rate by 13.6%, delay by 9.4%, and power by 14.7%.
- At a 20% threshold, RocketPPA improves area prediction by 9.6%, delay by 10.8%, and power by 18.5%.
- RocketPPA achieves over 20x speedup compared to MetRex and 30x over MasterRTL in processing the test set.
- RocketPPA's impact lies in potentially speeding up the hardware design process by providing accurate PPA estimations early on, reducing manual feature engineering overhead and time-consuming synthesis flows.
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