Hardware-Efficient Emulation of Leaky Integrate-and-Fire Model Using Template-Scaling-Based Exponential Function Approximation

被引:7
作者
Kim, Jeeson [1 ]
Kornijcuk, Vladmir [1 ]
Ye, Changmin [1 ]
Jeong, Doo Seok [1 ]
机构
[1] Hanyang Univ, Div Mat Sci & Engn, Seoul 04763, South Korea
基金
新加坡国家研究基金会;
关键词
Leaky integrate-and-fire model; spike-response model; template-scaling-based exponential function approximation; spiking neural network; FPGA IMPLEMENTATION; DIGITAL HARDWARE; NEURAL-NETWORKS; SPIKING; STDP; BACKPROPAGATION;
D O I
10.1109/TCSI.2020.3027583
中图分类号
TM [电工技术]; TN [电子技术、通信技术];
学科分类号
0808 ; 0809 ;
摘要
We present a method to emulate a leaky integrate-and-fire (LIF) model in a field-programmable gate array (FPGA) in a hardware-efficient manner. The simplified spike-response model (SRM0) is chosen as an LIF model. For the hardware-efficient implementation of SRM0, we adopt the template-scaling-based exponential function approximation (TS-EFA). This method allows high precision and low latency exponential function approximations with the efficient use of hardware resources. We subsequently propose an algorithm for SRM0, which leverages the advantage of TS-EFA. An implementation of 512 neurons conforming to SRM0 in an FPGA highlights (i) high precision of SRM0 emulation (mean squared error of membrane potential approximation: 4 x 10(-12) - 1 x 10(-10)), (ii) low latency (eight clock cycles), and (iii) high efficiency in hardware usage (only 125b memory per neuron).ardware usage (only 125b memory per neuron).
引用
收藏
页码:350 / 362
页数:13
相关论文
共 63 条
  • [1] Abbott L. F., 2001, THEORETICAL NEUROSCI
  • [2] [Anonymous], 2017, PROC IEEE INT S CIRC, DOI DOI 10.1109/ISCAS.2017.8050528
  • [3] THEORY FOR THE DEVELOPMENT OF NEURON SELECTIVITY - ORIENTATION SPECIFICITY AND BINOCULAR INTERACTION IN VISUAL-CORTEX
    BIENENSTOCK, EL
    COOPER, LN
    MUNRO, PW
    [J]. JOURNAL OF NEUROSCIENCE, 1982, 2 (01) : 32 - 48
  • [4] Error-backpropagation in temporally encoded networks of spiking neurons
    Bohte, SM
    Kok, JN
    La Poutré, H
    [J]. NEUROCOMPUTING, 2002, 48 : 17 - 37
  • [5] FPGA implementation of a biological neural network based on the Hodgkin-Huxley neuron model
    Bonabi, Safa Yaghini
    Asgharian, Hassan
    Safari, Saeed
    Ahmadabadi, Majid Nili
    [J]. FRONTIERS IN NEUROSCIENCE, 2014, 8
  • [6] Caron LC, 2011, IEEE INT SYMP CIRC S, P649
  • [7] Connectivity reflects coding: a model of voltage-based STDP with homeostasis
    Clopath, Claudia
    Buesing, Lars
    Vasilaki, Eleni
    Gerstner, Wulfram
    [J]. NATURE NEUROSCIENCE, 2010, 13 (03) : 344 - U19
  • [8] Loihi: A Neuromorphic Manycore Processor with On-Chip Learning
    Davies, Mike
    Srinivasa, Narayan
    Lin, Tsung-Han
    Chinya, Gautham
    Cao, Yongqiang
    Choday, Sri Harsha
    Dimou, Georgios
    Joshi, Prasad
    Imam, Nabil
    Jain, Shweta
    Liao, Yuyun
    Lin, Chit-Kwan
    Lines, Andrew
    Liu, Ruokun
    Mathaikutty, Deepak
    Mccoy, Steve
    Paul, Arnab
    Tse, Jonathan
    Venkataramanan, Guruguhanathan
    Weng, Yi-Hsin
    Wild, Andreas
    Yang, Yoonseok
    Wang, Hong
    [J]. IEEE MICRO, 2018, 38 (01) : 82 - 99
  • [9] de Dinechin Florent, 2010, Proceedings 2010 International Conference on Field-Programmable Technology (FPT 2010), P110, DOI 10.1109/FPT.2010.5681764
  • [10] FPGA-based implementation of a robust IEEE-754 exponential unit
    Doss, CC
    Riley, RL
    [J]. 12TH ANNUAL IEEE SYMPOSIUM ON FIELD-PROGRAMMABLE CUSTOM COMPUTING MACHINES, PROCEEDINGS, 2004, : 229 - 238