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