2021 conference paper
Fast and Accurate PPA Modeling with Transfer Learning
2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD). Presented at the 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD), Munich, Germany.
Event: 2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD) at Munich, Germany on November 1-4, 2021
author keywords: PPA; Machine Learning; Power; Performance; Area; Gradient Boost; Neural Network; Transfer Learning; Surrogate Modeling
TL;DR:
The approach reached the same PPA solution as human designers in the same or fewer runs for a CORTEX-M0 system design, showing potential for automating the recipe optimization without needing more runs than a human designer would need.
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Source: Web Of Science
Added: July 1, 2022
The power, performance and area (PPA) of digital blocks can vary 10:1 based on their synthesis, place, and route tool recipes. With rapid increase in number of PVT corners and complexity of logic functions approaching 10M gates, industry has an acute need to minimize the human resources, compute servers, and EDA licenses needed to achieve a Pareto optimal recipe. We first present models for fast accurate PPA prediction that can reduce the manual optimization iterations with EDA tools. Secondly we investigate techniques to automate the PPA optimization using evolutionary algorithms. For PPA prediction, a baseline model is trained on a known design using Latin hypercube sample runs of the EDA tool, and transfer learning is then used to train the model for an unseen design. For a known design the baseline needed 150 training runs to achieve a 95% accuracy. With transfer learning the same accuracy was achieved on a different (unseen) design in only 15 runs indicating the viability of transfer learning to generalize PPA models. The PPA optimization technique, based on evolutionary algorithms, effectively combines the PPA modeling and optimization. Our approach reached the same PPA solution as human designers in the same or fewer runs for a CORTEX-M0 system design. This shows potential for automating the recipe optimization without needing more runs than a human designer would need.