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arxiv:2408.09858

ShortCircuit: AlphaZero-Driven Circuit Design

Published on Aug 19
· Submitted by hba123 on Aug 20
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Abstract

Chip design relies heavily on generating Boolean circuits, such as AND-Inverter Graphs (AIGs), from functional descriptions like truth tables. While recent advances in deep learning have aimed to accelerate circuit design, these efforts have mostly focused on tasks other than synthesis, and traditional heuristic methods have plateaued. In this paper, we introduce ShortCircuit, a novel transformer-based architecture that leverages the structural properties of AIGs and performs efficient space exploration. Contrary to prior approaches attempting end-to-end generation of logic circuits using deep networks, ShortCircuit employs a two-phase process combining supervised with reinforcement learning to enhance generalization to unseen truth tables. We also propose an AlphaZero variant to handle the double exponentially large state space and the sparsity of the rewards, enabling the discovery of near-optimal designs. To evaluate the generative performance of our trained model , we extract 500 truth tables from a benchmark set of 20 real-world circuits. ShortCircuit successfully generates AIGs for 84.6% of the 8-input test truth tables, and outperforms the state-of-the-art logic synthesis tool, ABC, by 14.61% in terms of circuits size.

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By automating and optimizing chip design, we can meet AI's growing computational demands, enabling faster, more intelligent, and more efficient systems. Our method was tested on many circuits, generating compact and efficient designs with a success rate of 84.6%.
This work is an exciting step at the intersection of AI and hardware design, promising a future where AI powers software and optimises the hardware it runs on.

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