research-article

Neuromorphic Processors with Memristive Synapses: Synaptic Interface and Architectural Exploration

Authors:

Peng LiAuthors Info & Claims

ACM Journal on Emerging Technologies in Computing Systems (JETC), Volume 12, Issue 4

Article No.: 35, Pages 1 - 22

https://doi.org/10.1145/2894756

Published: 12 May 2016 Publication History

Abstract

Due to their nonvolatile nature, excellent scalability, and high density, memristive nanodevices provide a promising solution for low-cost on-chip storage. Integrating memristor-based synaptic crossbars into digital neuromorphic processors (DNPs) may facilitate efficient realization of brain-inspired computing. This article investigates architectural design exploration of DNPs with memristive synapses by proposing two synapse readout schemes. The key design tradeoffs involving different analog-to-digital conversions and memory accessing styles are thoroughly investigated. A novel storage strategy optimized for feedforward neural networks is proposed in this work, which greatly reduces the energy and area cost of the memristor array and its peripherals.

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Cited By

Zolfagharinejad MAlegre-Ibarra UChen TKinge Svan der Wiel W(2024)Brain-inspired computing systems: a systematic literature reviewThe European Physical Journal B10.1140/epjb/s10051-024-00703-697:6Online publication date: 6-Jun-2024
https://doi.org/10.1140/epjb/s10051-024-00703-6
Lee SHour LKim YHan Y(2024)ReQUSA: a novel ReRAM-based hardware accelerator architecture for high-speed quantum computer simulationPhysica Scripta10.1088/1402-4896/ad275299:3(035033)Online publication date: 26-Feb-2024
https://doi.org/10.1088/1402-4896/ad2752
Yayla MThomann SIslam MWei MHo SAziz AYang CChen JAmrouch H(2023)Reliable Brain-inspired AI Accelerators using Classical and Emerging Memories2023 IEEE 41st VLSI Test Symposium (VTS)10.1109/VTS56346.2023.10140068(1-10)Online publication date: 24-Apr-2023
https://doi.org/10.1109/VTS56346.2023.10140068
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Neuromorphic Processors with Memristive Synapses: Synaptic Interface and Architectural Exploration
1. Computer systems organization
  1. Architectures
    1. Other architectures
2. Hardware

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Published In

cover image ACM Journal on Emerging Technologies in Computing Systems

ACM Journal on Emerging Technologies in Computing Systems Volume 12, Issue 4

Regular Papers

July 2016

394 pages

ISSN:1550-4832

EISSN:1550-4840

DOI:10.1145/2856147

Editor:
Yuan Xie
University of California, Santa Barbara, USA

Issue’s Table of Contents

Copyright © 2016 ACM.

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Association for Computing Machinery

New York, NY, United States

Journal Family

ACM Journals for the Design of Smart and Connected Systems

Publication History

Published: 12 May 2016

Accepted: 01 February 2016

Revised: 01 January 2016

Received: 01 September 2015

Published in JETC Volume 12, Issue 4

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Cited By

Zolfagharinejad MAlegre-Ibarra UChen TKinge Svan der Wiel W(2024)Brain-inspired computing systems: a systematic literature reviewThe European Physical Journal B10.1140/epjb/s10051-024-00703-697:6Online publication date: 6-Jun-2024
https://doi.org/10.1140/epjb/s10051-024-00703-6
Lee SHour LKim YHan Y(2024)ReQUSA: a novel ReRAM-based hardware accelerator architecture for high-speed quantum computer simulationPhysica Scripta10.1088/1402-4896/ad275299:3(035033)Online publication date: 26-Feb-2024
https://doi.org/10.1088/1402-4896/ad2752
Yayla MThomann SIslam MWei MHo SAziz AYang CChen JAmrouch H(2023)Reliable Brain-inspired AI Accelerators using Classical and Emerging Memories2023 IEEE 41st VLSI Test Symposium (VTS)10.1109/VTS56346.2023.10140068(1-10)Online publication date: 24-Apr-2023
https://doi.org/10.1109/VTS56346.2023.10140068
Cheon SLee KPark J(2023)A 2941-TOPS/W Charge-Domain 10T SRAM Compute-in-Memory for Ternary Neural NetworkIEEE Transactions on Circuits and Systems I: Regular Papers10.1109/TCSI.2023.324138570:5(2085-2097)Online publication date: May-2023
https://doi.org/10.1109/TCSI.2023.3241385
Kim YKim HKim J(2022)Extreme Partial-Sum Quantization for Analog Computing-In-Memory Neural Network AcceleratorsACM Journal on Emerging Technologies in Computing Systems10.1145/352810418:4(1-19)Online publication date: 13-Oct-2022
https://dl.acm.org/doi/10.1145/3528104
Choi WKwak MKim SHwang H(2021)Neural Network Training Acceleration With RRAM-Based Hybrid SynapsesFrontiers in Neuroscience10.3389/fnins.2021.69041815Online publication date: 24-Jun-2021
https://doi.org/10.3389/fnins.2021.690418
Kim YKim HPark JOh HKim J(2021)Mapping Binary ResNets on Computing-In-Memory Hardware with Low-bit ADCs2021 Design, Automation & Test in Europe Conference & Exhibition (DATE)10.23919/DATE51398.2021.9474056(856-861)Online publication date: 1-Feb-2021
https://doi.org/10.23919/DATE51398.2021.9474056
Shin TPark SKim SKim SSon KPark HLho DCho KPark GGong KJung SKim J(2021)Signal Integrity Modeling and Analysis of Large-Scale Memristor Crossbar Array in a High-Speed Neuromorphic System for Deep Neural NetworkIEEE Transactions on Components, Packaging and Manufacturing Technology10.1109/TCPMT.2021.309274011:7(1122-1136)Online publication date: Jul-2021
https://doi.org/10.1109/TCPMT.2021.3092740
Jang YKang GKim TSeo YLee KPark BPark J(2021)Stochastic SOT device based SNN architecture for On-chip Unsupervised STDP LearningIEEE Transactions on Computers10.1109/TC.2021.3119180(1-1)Online publication date: 2021
https://doi.org/10.1109/TC.2021.3119180
Azamat AAsim FLee J(2021)Quarry: Quantization-based ADC Reduction for ReRAM-based Deep Neural Network Accelerators2021 IEEE/ACM International Conference On Computer Aided Design (ICCAD)10.1109/ICCAD51958.2021.9643502(1-7)Online publication date: 1-Nov-2021
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