ShortCircuit: AlphaZero-Driven Circuit Design

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 transformerbased 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 realworld circuits. ShortCircuit successfully generates AIGs for84.6% 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. The rapid proliferation of AI has triggered an unprecedented surge in computational demands, exceeding the capabilities of existing hardware and thereby becoming a major bottleneck to AI's continued growth. To enable further advancements in AI and other domains, the development of new hardware is crucial. Chip design plays a pivotal role in this development effort allowing the nextgeneration of computing systems to emerge. However, traditional methodologies struggle to keep pace with the accelerating demands, underscoring the need for innovative approaches to accelerate the design process and discover novel architectures. At its core, a chip is the physical embodiment of a Boolean function, transforming binary inputs into desired outputs.