title: ShortCircuit: AlphaZero-Driven Circuit Design
summary: 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.
paper_authors._id: 66c4433df42773a6f11dd8e8
paper_authors.name: Dimitrios Tsaras
paper_authors._id: 66c4433df42773a6f11dd8e9
paper_authors.name: Antoine Grosnit
paper_authors._id: 66c4433df42773a6f11dd8ea
paper_authors.name: Lei Chen
paper_authors._id: 66c4433df42773a6f11dd8eb
paper_authors.name: Zhiyao Xie
paper_authors._id: 66c4433df42773a6f11dd8ec
paper_authors.name: Haitham Bou-Ammar
paper_authors.status: claimed_verified
paper_authors.statusLastChangedAt: 2024-09-23T13:07:13.581Z
paper_authors._id: 631c375768f7da9ad2496bf6
paper_authors.avatarUrl: https://cdn-avatars.huggingface.co/v1/production/uploads/631c375768f7da9ad2496bf6/1sDOoecA6e1v_hn_VAgUq.jpeg
paper_authors.fullname: Haitham Bou Ammar
paper_authors.type: user
paper_authors.user: hba123
paper_authors._id: 66c4433df42773a6f11dd8ed
paper_authors.name: Mingxuan Yuan
paper_title: ShortCircuit: AlphaZero-Driven Circuit Design
paper_summary: 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.
paper_ai_summary: ShortCircuit, a transformer-based architecture, uses supervised and reinforcement learning to generate optimal AIGs from truth tables, outperforming traditional methods in circuit size.
paper_ai_keywords: transformer-based architecture AND-Inverter Graphs (AIGs) truth tables end-to-end generation supervised learning reinforcement learning AlphaZero variant state space reward sparsity