AC-Refiner: Efficient Arithmetic Circuit Optimization Using Conditional Diffusion Models

TL;DR

AC-Refiner introduces a novel framework using conditional diffusion models to optimize arithmetic circuits, significantly improving design quality and Pareto optimality by framing circuit synthesis as a conditional image generation task.

Contribution

It pioneers the use of conditional diffusion models for arithmetic circuit optimization, enabling more effective exploration of high-quality design variants.

Findings

Outperforms state-of-the-art baselines in Pareto optimality.

Generates higher quality circuit designs.

Enhances practical application performance.

Abstract

Arithmetic circuits, such as adders and multipliers, are fundamental components of digital systems, directly impacting the performance, power efficiency, and area footprint. However, optimizing these circuits remains challenging due to the vast design space and complex physical constraints. While recent deep learning-based approaches have shown promise, they struggle to consistently explore high-potential design variants, limiting their optimization efficiency. To address this challenge, we propose AC-Refiner, a novel arithmetic circuit optimization framework leveraging conditional diffusion models. Our key insight is to reframe arithmetic circuit synthesis as a conditional image generation task. By carefully conditioning the denoising diffusion process on target quality-of-results (QoRs), AC-Refiner consistently produces high-quality circuit designs. Furthermore, the explored designs are used to fine-tune the diffusion model, which focuses the exploration near the Pareto frontier. Experimental results demonstrate that AC-Refiner generates designs with superior Pareto optimality, outperforming state-of-the-art baselines. The performance gain is further validated by integrating AC-Refiner into practical applications.