短路：由AlphaZero驱动的电路设计

ShortCircuit: AlphaZero-Driven Circuit Design

August 19, 2024

作者: Dimitrios Tsaras, Antoine Grosnit, Lei Chen, Zhiyao Xie, Haitham Bou-Ammar, Mingxuan Yuan

cs.AI

摘要

芯片设计在很大程度上依赖于从功能描述（如真值表）生成布尔电路，例如与反相器图（AIGs）。虽然近年来深度学习方面取得了进展，旨在加速电路设计，但这些努力大多集中在综合之外的任务上，传统的启发式方法已经达到瓶颈。在本文中，我们介绍了ShortCircuit，这是一种新颖的基于Transformer的架构，利用AIGs的结构特性进行高效的空间探索。与先前尝试使用深度网络端到端生成逻辑电路的方法相反，ShortCircuit采用了一个两阶段过程，结合监督学习和强化学习，以增强对未见真值表的泛化能力。我们还提出了AlphaZero的变体，以处理双指数级别的状态空间和奖励的稀疏性，从而发现接近最优设计。为了评估我们训练模型的生成性能，我们从一个包含20个真实电路的基准集中提取了500个真值表。ShortCircuit成功为8输入测试真值表中的84.6%生成了AIGs，并在电路规模方面比当今最先进的逻辑综合工具ABC提高了14.61%。

English

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.