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

2025-01-06

Computing and Communication Workshop and Conference

AI Summary

Phase-change memory (PCM) faces two critical bottlenecks—high write energy consumption and limited cell endurance—that hinder its adoption as a DRAM replacement. To address these challenges, this work proposes an adaptive learning–based online write optimization framework that jointly leverages a lightweight neural network for real-time sensing of device state and aging characteristics, and deep reinforcement learning for dynamic selection of optimal write parameters. The framework ensures data reliability while simultaneously optimizing energy efficiency and write performance. Experimental evaluation demonstrates that, compared to baseline approaches, the proposed method achieves an average 63% reduction in write energy, up to a 51% improvement in write throughput, and significantly extends PCM device lifetime. This work establishes a deployable intelligent control paradigm for high-efficiency, long-endurance PCM systems.

Technology Category

• Search and Optimization: Learning to Search
• Natural Language Processing: Learning & Optimization for NLP
• Machine Learning: Life-Long and Continual Learning

Application Category

• Systems and Infrastructure for Web, Mobile and WoT: Energy management for devices in mobile Web and WoT environments
• User Modeling, Personalization and Recommendation: On-Device user modeling, personalization, and recommendation
• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking

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.

Problem

• Optimizing write energy consumption in phase-change memory
• Enhancing PCM cell endurance through adaptive parameter adjustment
• Reducing performance limitations of PCM for data storage applications

Innovation

• Neural networks monitor real-time PCM operating conditions
• Reinforcement learning dynamically adjusts write parameters
• Adaptive optimization reduces energy and improves performance