Best Memory Architecture Exploration under Parameters Variations accelerated with Machine Learning

TL;DR

This paper introduces machine learning-accelerated algorithms for efficiently exploring optimal memory architectures under parameter variations, significantly reducing search time while maintaining high accuracy.

Contribution

It proposes Best Arm Identification algorithms to rapidly find the best memory organization on average, addressing the challenge of parameter variability in memory design.

Findings

Achieves 99% accuracy in identifying optimal memory architecture

Reduces exploration time by a factor of 5 compared to exhaustive search

Effectively handles process variation and dynamic voltage scaling effects

Abstract

The design of effective memory architecture is of utmost importance in modern computing systems. However, the design of memory subsystems is even more difficult today because process variation and modern design techniques like dynamic voltage scaling make performance metrics for memory assessment be treated as random variables instead of scalars at design time. Most of the previous works have studied the design of memory design from the yield analysis perspective leaving the question of the best memory organization on average open. Because examining all possible combinations of design parameter values of a memory chip would require prohibitively much time, in this work, we propose Best Arm Identification (BAI) algorithms to accelerate the exploration for the best memory architecture on average under parameter variations. Our experimental results demonstrate that we can arrive at the best memory organization 99% of the time in x5 faster than an exhaustive search of all possible conditions.