The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
인메모리 계산으로 구동되는 CNN(Convolutional Neural Network)을 가속화하기 위해 효율적인 ReRAM(Resistive Random Access Memory) 구조가 개발되었습니다. 새로운 ReRAM 셀 회로는 양방향(2-D) 접근성을 갖도록 설계되었습니다. 전체 메모리 시스템은 2차원 배열로 구성되어 있으며, 여기서 특정 메모리 셀은 열 및 행 지역 모두에서 동일하게 액세스할 수 있습니다. CNN의 메모리 내 계산을 위해 동일한 하위 배열의 관련 셀만 2D 읽기 작업으로 액세스되며 이는 기존 ReRAM 셀에서는 거의 구현되지 않습니다. 이러한 방식으로 기존 ReRAM 구조의 중복 액세스(열 또는 행)를 방지하여 CNN을 메모리 내에서 처리할 때 불필요한 데이터 이동을 제거합니다. 시뮬레이션 결과에서 제안된 메모리 구조의 에너지 및 대역폭 효율성은 각각 최첨단 ReRAM 아키텍처의 1.4배 및 5배입니다.
Yan CHEN
Hunan University,Nara Institute of Science and Technology
Jing ZHANG
Hunan University
Yuebing XU
Hunan University
Yingjie ZHANG
Hunan University
Renyuan ZHANG
Nara Institute of Science and Technology
Yasuhiko NAKASHIMA
Nara Institute of Science and Technology
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Yan CHEN, Jing ZHANG, Yuebing XU, Yingjie ZHANG, Renyuan ZHANG, Yasuhiko NAKASHIMA, "A ReRAM-Based Row-Column-Oriented Memory Architecture for Convolutional Neural Networks" in IEICE TRANSACTIONS on Electronics,
vol. E102-C, no. 7, pp. 580-584, July 2019, doi: 10.1587/transele.2018CTS0001.
Abstract: An efficient resistive random access memory (ReRAM) structure is developed for accelerating convolutional neural network (CNN) powered by the in-memory computation. A novel ReRAM cell circuit is designed with two-directional (2-D) accessibility. The entire memory system is organized as a 2-D array, in which specific memory cells can be identically accessed by both of column- and row-locality. For the in-memory computations of CNNs, only relevant cells in an identical sub-array are accessed by 2-D read-out operations, which is hardly implemented by conventional ReRAM cells. In this manner, the redundant access (column or row) of the conventional ReRAM structures is prevented to eliminated the unnecessary data movement when CNNs are processed in-memory. From the simulation results, the energy and bandwidth efficiency of the proposed memory structure are 1.4x and 5x of a state-of-the-art ReRAM architecture, respectively.
URL: https://global.ieice.org/en_transactions/electronics/10.1587/transele.2018CTS0001/_p
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@ARTICLE{e102-c_7_580,
author={Yan CHEN, Jing ZHANG, Yuebing XU, Yingjie ZHANG, Renyuan ZHANG, Yasuhiko NAKASHIMA, },
journal={IEICE TRANSACTIONS on Electronics},
title={A ReRAM-Based Row-Column-Oriented Memory Architecture for Convolutional Neural Networks},
year={2019},
volume={E102-C},
number={7},
pages={580-584},
abstract={An efficient resistive random access memory (ReRAM) structure is developed for accelerating convolutional neural network (CNN) powered by the in-memory computation. A novel ReRAM cell circuit is designed with two-directional (2-D) accessibility. The entire memory system is organized as a 2-D array, in which specific memory cells can be identically accessed by both of column- and row-locality. For the in-memory computations of CNNs, only relevant cells in an identical sub-array are accessed by 2-D read-out operations, which is hardly implemented by conventional ReRAM cells. In this manner, the redundant access (column or row) of the conventional ReRAM structures is prevented to eliminated the unnecessary data movement when CNNs are processed in-memory. From the simulation results, the energy and bandwidth efficiency of the proposed memory structure are 1.4x and 5x of a state-of-the-art ReRAM architecture, respectively.},
keywords={},
doi={10.1587/transele.2018CTS0001},
ISSN={1745-1353},
month={July},}
부
TY - JOUR
TI - A ReRAM-Based Row-Column-Oriented Memory Architecture for Convolutional Neural Networks
T2 - IEICE TRANSACTIONS on Electronics
SP - 580
EP - 584
AU - Yan CHEN
AU - Jing ZHANG
AU - Yuebing XU
AU - Yingjie ZHANG
AU - Renyuan ZHANG
AU - Yasuhiko NAKASHIMA
PY - 2019
DO - 10.1587/transele.2018CTS0001
JO - IEICE TRANSACTIONS on Electronics
SN - 1745-1353
VL - E102-C
IS - 7
JA - IEICE TRANSACTIONS on Electronics
Y1 - July 2019
AB - An efficient resistive random access memory (ReRAM) structure is developed for accelerating convolutional neural network (CNN) powered by the in-memory computation. A novel ReRAM cell circuit is designed with two-directional (2-D) accessibility. The entire memory system is organized as a 2-D array, in which specific memory cells can be identically accessed by both of column- and row-locality. For the in-memory computations of CNNs, only relevant cells in an identical sub-array are accessed by 2-D read-out operations, which is hardly implemented by conventional ReRAM cells. In this manner, the redundant access (column or row) of the conventional ReRAM structures is prevented to eliminated the unnecessary data movement when CNNs are processed in-memory. From the simulation results, the energy and bandwidth efficiency of the proposed memory structure are 1.4x and 5x of a state-of-the-art ReRAM architecture, respectively.
ER -