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논문 기본 정보

자료유형
학술저널
저자정보
Hyokeun Lee (Seoul National University) Hyuk-Jae Lee (Seoul National University) Hyun Kim (Seoul National University of Science and Technology)
저널정보
대한전자공학회 JOURNAL OF SEMICONDUCTOR TECHNOLOGY AND SCIENCE Journal of Semiconductor Technology and Science Vol.22 No.4
발행연도
2022.8
수록면
216 - 223 (8page)
DOI
10.5573/JSTS.2022.22.4.216

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초록· 키워드

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Phase-change memory (PCM) garners attention as the most promising nonvolatile memory (NVM). In particular, PCM is suitable for applications that are not memory intensive, and the convolutional neural network (CNN) inference is widely known as a representative computation-intensive model. Therefore, CNN inference seems to be very suitable for a PCM-based system. However, the PCM suffers from the characteristic of being vulnerable to disturbance errors. In particular, read disturbance error (RDE) becomes a serious problem for workloads involving a large number of zeros, and unfortunately, matrices in CNN are sparse, which inevitably incurs a significant amount of RDEs. In this paper, we present an RDE-tolerant PCM-based system for CNN inference workloads. The proposed method restores vulnerable data words by leveraging a dedicated SRAM-based table. Furthermore, we also propose a replacement policy, which detects non-urgent entries, by utilizing the contents (i.e., counters) in the table. As a result, the proposed method significantly reduces RDEs with minor speed degradation.

목차

Abstract
I. INTRODUCTION
II. READ DISTURBANCE IN PCM
III. RSA: READ DISTURBANCE SCRUBBING ASSISTANCE
IV. EVALUATION
V. CONCLUSIONS
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