短路：以AlphaZero為基礎的電路設計

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

晶片設計在很大程度上依賴於從功能描述（如真值表）生成布林電路，例如AND-Inverter Graphs（AIGs）。儘管近年來深度學習方面取得了進展，旨在加快電路設計，但這些努力主要集中在除合成之外的任務上，而傳統的啟發式方法已經達到瓶頸。在本文中，我們介紹了ShortCircuit，這是一種新型基於Transformer的架構，利用AIGs的結構特性並執行有效的空間探索。與先前試圖使用深度網絡端到端生成邏輯電路不同，ShortCircuit採用了一個結合監督和強化學習的兩階段過程，以增強對未見真值表的泛化能力。我們還提出了一種AlphaZero變體，以應對雙指數級大的狀態空間和獎勵的稀疏性，從而實現對近乎最佳設計的發現。為了評估我們訓練模型的生成性能，我們從一組20個真實電路中提取了500個真值表。ShortCircuit成功為8輸入測試真值表中的84.6%生成了AIGs，並在電路大小方面比當前最先進的邏輯綜合工具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.