Journal of the Semiconductor & Display Technology (반도체디스플레이기술학회지)

The Korean Society Of Semiconductor & Display Technology (한국반도체디스플레이기술학회)
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Lifetime Extension Method for Non-Volatile Memory based Deep Learning System by analyzing Data Write Pattern

데이터 쓰기 패턴 분석을 통한 비휘발성 메모리 기반 딥러닝 시스템의 수명 연장 기법

  • Choi, Juhee (Dept. of Smart Information Communication Engineering, Sangmyung University)
  • 최주희 (상명대학교 스마트정보통신공학과)
  • Received : 2022.07.25
  • Accepted : 2022.09.19
  • Published : 2022.09.30
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Abstract

Modern computer systems usually have special hardware for operations used in deep learning workload even edge computing environment. Non-volatile memories (NVMs) have been considered for alternative memory storage because they consume little static energy and occupy small area. However, there is a problem for NVMs to be directly adopted. An NVM cell has limited write endurance, so that the lifetime of NVM-based memory system is much shorter than that of conventional memory system. To overcome this problem for the deep learning system, this paper proposes a novel method to extend the lifetime based on the analysis of the deep learning workloads. If an incoming block has more than a predefined number of frequently used values, the cacheline is defined as write friendly block. During the victim selection, the cacheline has lower possibility to be chosen as victim. The experimental results show that the lifetime is increased by about 50% and energy consumption is decreased by 3% with a little performance hurt.

Keywords

Acknowledgement

본 연구는 2021년도 과학기술정보통신부의 재원으로 한국연구재단의 지원을 받은 기초연구사업 연구임(NRF-2021R1G1A1004340).

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