Logic Synthesis Optimization with Predictive Self-Supervision via Causal Transformers

TL;DR

This paper introduces LSOformer, a transformer-based model with predictive self-supervision for logic synthesis optimization, improving QoR prediction accuracy and generalization in EDA workflows.

Contribution

The paper presents LSOformer, a novel autoregressive transformer model with predictive SSL, addressing overfitting and generalization issues in ML-guided logic synthesis.

Findings

Achieves up to 17.06% improvement in QoR prediction accuracy.

Outperforms baseline models on multiple circuit datasets.

Demonstrates effectiveness of cross-attention in integrating circuit and optimization data.

Abstract

Contemporary hardware design benefits from the abstraction provided by high-level logic gates, streamlining the implementation of logic circuits. Logic Synthesis Optimization (LSO) operates at one level of abstraction within the Electronic Design Automation (EDA) workflow, targeting improvements in logic circuits with respect to performance metrics such as size and speed in the final layout. Recent trends in the field show a growing interest in leveraging Machine Learning (ML) for EDA, notably through ML-guided logic synthesis utilizing policy-based Reinforcement Learning (RL) methods.Despite these advancements, existing models face challenges such as overfitting and limited generalization, attributed to constrained public circuits and the expressiveness limitations of graph encoders. To address these hurdles, and tackle data scarcity issues, we introduce LSOformer, a novel approach harnessing Autoregressive transformer models and predictive SSL to predict the trajectory of Quality of Results (QoR). LSOformer integrates cross-attention modules to merge insights from circuit graphs and optimization sequences, thereby enhancing prediction accuracy for QoR metrics. Experimental studies validate the effectiveness of LSOformer, showcasing its superior performance over baseline architectures in QoR prediction tasks, where it achieves improvements of 5.74%, 4.35%, and 17.06% on the EPFL, OABCD, and proprietary circuits datasets, respectively, in inductive setup.