Learning optimal erasure of a Static Random Access Memory

TL;DR

This paper demonstrates that it is possible to erase static RAM at high speed with finite heat dissipation by optimizing protocols, challenging the belief that faster erasure always causes more heat, and provides design principles for future memories.

Contribution

It introduces a novel approach combining stochastic thermodynamics and machine learning to optimize RAM erasure, showing finite heat dissipation at arbitrary speeds and deriving explicit design principles.

Findings

Fast RAM erasure with finite heat dissipation is achievable.

Optimized protocols minimize heat dissipation during erasure.

Bridges stochastic thermodynamics with electronic engineering applications.

Abstract

In this paper, we study the thermodynamic cost associated with erasing a static random access memory. By combining the stochastic thermodynamics framework of electronic circuits with machine learning-based optimization techniques, we show that it is possible to erase an electronic random access memory at arbitrarily fast speed and finite heat dissipation. This disproves a widely held belief that heat dissipation scales linearly with erasure speed. Furthermore, we find driving protocols that minimize the heat dissipation, leading to explicit design principles for future computer memories. This bridges an important gap between the theoretical framework of stochastic thermodynamics and applications in electronic engineering.