Multi-objective Optimisation of Digital Circuits based on Cell Mapping in an Industrial EDA Flow

TL;DR

This paper presents a fully-automated multi-objective EDA flow that globally optimizes digital circuit design by tuning drive strength mapping, resulting in improved power, performance, and area metrics compared to standard flows.

Contribution

It introduces a novel multi-objective population-based search framework for block-level digital circuit optimization within an industrial EDA flow.

Findings

Significant improvements in power, performance, and area metrics.

Enhanced solutions over standard digital flow.

Effective application to benchmark circuits.

Abstract

Modern electronic design automation (EDA) tools can handle the complexity of state-of-the-art electronic systems by decomposing them into smaller blocks or cells, introducing different levels of abstraction and staged design flows. However, throughout each independent-optimised design step, overhead and inefficiency can accumulate in the resulting overall design. Performing design-specific optimisation from a more global viewpoint requires more time due to the larger search space, but has the potential to provide solutions with improved performance. In this work, a fully-automated, multi-objective (MO) EDA flow is introduced to address this issue. It specifically tunes drive strength mapping, preceding physical implementation, through multi-objective population-based search algorithms. Designs are evaluated with respect to their power, performance and area (PPA). The proposed approach is aimed at digital circuit optimisation at the block-level, where it is capable of expanding the design space and offers a set of trade-off solutions for different case-specific utilisation. We have applied the proposed MOEDA framework to ISCAS-85 and EPFL benchmark circuits using a commercial 65nm standard cell library. The experimental results demonstrate how the MOEDA flow enhances the solutions initially generated by the standard digital flow, and how simultaneously a significant improvement in PPA metrics is achieved.