@inproceedings{
song2023circuitvae,
title={Circuit{VAE}: Efficient and Scalable Latent Circuit Optimization},
author={Jialin Song and Aidan Swope and Robert Kirby and Rajarshi Roy and Saad Godil and Jonathan Raiman and Bryan Catanzaro},
booktitle={NeurIPS 2023 Workshop on Adaptive Experimental Design and Active Learning in the Real World},
year={2023},
url={https://openreview.net/forum?id=FpMRFG3z2Q}
}
Automatically designing fast and space-efficient digital circuits is challenging because circuits are discrete, must exactly implement the desired logic, and are costly to simulate. We address these challenges with CircuitVAE, a search algorithm that embeds computation graphs in a continuous space and optimizes a learned surrogate of physical simulation by gradient descent. By carefully controlling overfitting of the simulation surrogate and ensuring diverse exploration, our algorithm is highly sample-efficient, yet gracefully scales to large problem instances and high sample budgets. We test CircuitVAE by designing binary adders across a large range of sizes, IO timing constraints, and sample budgets. Our method excels at designing large circuits, where other algorithms struggle: compared to reinforcement learning and genetic algorithms, CircuitVAE typically finds 64-bit adders which are smaller and faster using less than half the sample budget. We also find CircuitVAE can design state-of-the-art adders in a real-world chip, demonstrating that our method can outperform commercial tools in a realistic setting.
CircuitVAE: Efficient and Scalable Latent Circuit Optimization
Jialin Song Aidan Swope Robert Kirby Rajarshi Roy Saad Godil Jonathan Raiman Bryan Catanzaro