Machine Learning-Driven Paradigms in VLSI Design: Exploring the Synergy with Pruning for Optimal Circuit Efficiency

Machine Learning-Driven Paradigms in VLSI Design: Exploring the Synergy with Pruning for Optimal Circuit Efficiency

Vaishnavi Shinde, Saurav Sahane, Pranav Kedari, Neha Singh

Electronics and Telecommunication Engineering,

ISBM College Of Engineering

Pune, India

Abstract—This paper investigates the incorporation of machine learning (ML) techniques in Very Large-Scale Integration (VLSI) design, concentrating on pruning methods to boost circuit efficiency. By utilizing ML algorithms for feature extraction, optimization, and decision-making while applying pruning methods to eliminate redundancy, our study introduces a unique strategy for achieving superior circuit performance. Key contributions include a thorough methodology that describes ML- based pruning in hardware design along with an assessment of its effects on design metrics like area, power, and speed. The findings indicate substantial advancements, implying that ML-based pruning could transform VLSI design processes.

Keywords— Machine Learning (ML), Very Large- Scale Integration (VLSI), Pruning Techniques, Circuit Efficiency, ML-Driven Pruning