System on Chip Testbed for Deep Neuromorphic Neural Networks

被引:1
|
作者
Rodriguez, Nicolas [1 ]
Villemur, Martin [1 ]
Klepatsch, Daniel [1 ]
Ivanovich, Diego Gigena [1 ]
Julian, Pedro [1 ]
机构
[1] Silicon Austria Labs, Altenberger Str 66c, Linz, Austria
来源
2023 IEEE INTERNATIONAL SYMPOSIUM ON CIRCUITS AND SYSTEMS, ISCAS | 2023年
关键词
VLSI; Neuromorphic Computing; Neural Network Accelerators; CMOS;
D O I
10.1109/ISCAS46773.2023.10182079
中图分类号
TP18 [人工智能理论];
学科分类号
081104 ; 0812 ; 0835 ; 1405 ;
摘要
This paper describes a first prototype of a testbed System on chip (SoC) to design and evaluate different Neuromorphic Deep Neural Networks (NN) cores. The 1.25mmx1.25mm SoC was fabricated in a 65nm CMOS technology and implements a system composed of an ARM based microprocessor, two memory banks of 32KB, a QSPI serial interface and two NN accelerators. The first one is a novel neuromorphic accelerator consisting of a 5x5 kernel Symmetrical Simplicial (SymSimp) core with a depthwise separable structure, which allows to efficiently implement multi-channel convolutional layers by breaking 3D kernels into 2D kernels. The second is a 3x3 conventional MAC engine to implement the fully connected layers. Experimental results show an energy efficiency of 0.49pJ/OP, which is competitive when compared to similar technology ICs, and extrapolated to the MobileNetworkV2 ImageNet represents a factor of 2 improvement with respect to NVIDIA Jetson Nano.
引用
收藏
页数:5
相关论文
共 50 条
  • [21] Deep Neural Networks for the Recognition and Classification of Heart Murmurs Using Neuromorphic Auditory Sensors
    Dominguez-Morales, Juan P.
    Jimenez-Fernandez, Angel F.
    Dominguez-Morales, Manuel J.
    Jimenez-Moreno, Gabriel
    IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS, 2018, 12 (01) : 24 - 34
  • [22] Neural networks on a chip
    Lau, C
    SIXTH, SEVENTH, AND EIGHTH WORKSHOPS ON VIRTUAL INTELLIGENCE: ACADEMIC/INDUSTRIAL/NASA/DEFENSE TECHNICAL INTERCHANGE AND TUTORIALS, 1996, 2878 : 450 - 473
  • [23] End-to-End Implementation of Various Hybrid Neural Networks on a Cross-Paradigm Neuromorphic Chip
    Wang, Guanrui
    Ma, Songchen
    Wu, Yujie
    Pei, Jing
    Zhao, Rong
    Shi, Luping
    FRONTIERS IN NEUROSCIENCE, 2021, 15
  • [24] Networks-on-Chip based Deep Neural Networks Accelerators for IoT Edge Devices
    Ascia, Giuseppe
    Catania, Vincenzo
    Monteleone, Salvatore
    Palesi, Maurizio
    Patti, Davide
    Jose, John
    2019 SIXTH INTERNATIONAL CONFERENCE ON INTERNET OF THINGS: SYSTEMS, MANAGEMENT AND SECURITY (IOTSMS), 2019, : 227 - 234
  • [25] Efficient Hardware Optimization Strategies for Deep Neural Networks Acceleration Chip
    Zhang Meng
    Zhang Jingwei
    Li Guoqing
    Wu Ruixia
    Zeng Xiaoyang
    JOURNAL OF ELECTRONICS & INFORMATION TECHNOLOGY, 2021, 43 (06) : 1510 - 1517
  • [26] Dynamic Energy Optimization in Chip Multiprocessors Using Deep Neural Networks
    Moghaddam, Milad Ghorbani
    Guan, Wenkai
    Ababei, Cristinel
    IEEE TRANSACTIONS ON MULTI-SCALE COMPUTING SYSTEMS, 2018, 4 (04): : 649 - 661
  • [27] A Novel Electronic Chip Detection Method Using Deep Neural Networks
    Zhang, Huiyan
    Sun, Hao
    Shi, Peng
    Minchala, Luis Ismael
    MACHINES, 2022, 10 (05)
  • [28] DANoC: An Efficient Algorithm and Hardware Codesign of Deep Neural Networks on Chip
    Zhou, Xichuan
    Li, Shengli
    Tang, Fang
    Hu, Shengdong
    Lin, Zhi
    Zhang, Lei
    IEEE TRANSACTIONS ON NEURAL NETWORKS AND LEARNING SYSTEMS, 2018, 29 (07) : 3176 - 3187
  • [29] A Neuromorphic Computing System for Bitwise Neural Networks Based on ReRAM Synaptic Array
    Li, Pin-Yi
    Yang, Cheng-Han
    Chen, Wei-Hao
    Huang, Jian-Hao
    Wei, Wei-Chen
    Liu, Je-Syu
    Lin, Wei-Yu
    Hsu, Tzu-Hsiang
    Hsieh, Chih-Cheng
    Liu, Ren-Shuo
    Chang, Meng-Fan
    Tang, Kea-Tiong
    2018 IEEE BIOMEDICAL CIRCUITS AND SYSTEMS CONFERENCE (BIOCAS): ADVANCED SYSTEMS FOR ENHANCING HUMAN HEALTH, 2018, : 615 - 618
  • [30] Synergy Exploration in Deploying Convolutional Neural Networks Across Distributed Neuromorphic System
    Gong, Bo
    Wang, Jiang
    Chang, Siyuan
    Liu, Weitong
    Li, Tong
    2024 IEEE INTERNATIONAL INSTRUMENTATION AND MEASUREMENT TECHNOLOGY CONFERENCE, I2MTC 2024, 2024,