SMART-WRITE: Adaptive Learning-based Write Energy Optimization for Phase Change Memory

TL;DR

SMART-WRITE uses adaptive neural networks and reinforcement learning to optimize write energy and performance in phase-change memory, significantly reducing energy use and enhancing lifespan.

Contribution

It introduces a novel adaptive learning-based framework combining neural networks and reinforcement learning for real-time PCM write optimization.

Findings

Reduces write energy consumption by up to 63%.

Improves performance by up to 51%.

Demonstrates effective real-time adjustment of write parameters.

Abstract

As dynamic random access memory (DRAM) and other current transistor-based memories approach their scalability limits, the search for alternative storage methods becomes increasingly urgent. Phase-change memory (PCM) emerges as a promising candidate due to its scalability, fast access time, and zero leakage power compared to many existing memory technologies. However, PCM has significant drawbacks that currently hinder its viability as a replacement. PCM cells suffer from a limited lifespan because write operations degrade the physical material, and these operations consume a considerable amount of energy. For PCM to be a practical option for data storage-which involves frequent write operations-its cell endurance must be enhanced, and write energy must be reduced. In this paper, we propose SMART-WRITE, a method that integrates neural networks (NN) and reinforcement learning (RL) to dynamically optimize write energy and improve performance. The NN model monitors real-time operating conditions and device characteristics to determine optimal write parameters, while the RL model dynamically adjusts these parameters to further optimize PCM's energy consumption. By continuously adjusting PCM write parameters based on real-time system conditions, SMART-WRITE reduces write energy consumption by up to 63% and improves performance by up to 51% compared to the baseline and previous models.