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
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