Hardware Implementation and Discharge Performance Comparison of Nerve Synapses Based on FPGA
Chen Kai1,2, Lu Mai1*, Yi Feihong1,2, Wang Chao1
1(Key Lab of Opto-Electronic Technology and Intelligent Control of Ministry of Education, Lanzhou Jiaotong University, Lanzhou 730070, China) 2(The School of Automatization & Electric Engineering, Lanzhou Jiaotong University, Lanzhou 730070, China)
Abstract：At present, there are few studies on the hardware implementation of chemical synaptic, the use of FPGA chip to realize chemical synapses is of great value to the realization of neural network hardware. In this work, based on the DSP Builder model, the DSP Builder model of the chemical synapse was reasonably splited, and then each module was compiled and run in the software environment corresponding to the FPGA, and finally downloaded to the FPGA core chip, using the FPGA hardware to realize five kinds of chemical synapses based on different mechanisms. The correlation coefficient method was used to compare the pre-synaptic neuron action potential, post-synaptic neuron action potential and synaptic current amplitude between the simulation results and the hardware results in the same cycle. Five chemical synapses realized by hardware could better transmit action potentials, but each model consumed different resources. The internal multiplier resources consumed by Model 3 (69%) were about 2 times that of Model 5 (31%), indicating that the higher the mathematical complexity of the synapse model, the more multiplier resources it consumed. The comparison of the correlation coefficient method showed that the correlation of model 3 was the highest, which was 0.791 3, and the correlation coefficient of model 4 was the lowest, which was 0.693 5. Although Model 3 had the high mathematical complexity and hardware resource consumption, its performance was the best. The five synaptic models implemented by hardware could better represent the one-way transmission of chemical synapses. The model 5 had low hardware resource consumption and high correlation. The model 5 was recommended as the first choice for hardware realization of chemical synapses.
陈凯, 逯迈, 易飞鸿, 王超. 基于FPGA的神经突触的硬件实现及放电性能比较[J]. 中国生物医学工程学报, 2020, 39(1): 57-66.
Chen Kai, Lu Mai, Yi Feihong, Wang Chao. Hardware Implementation and Discharge Performance Comparison of Nerve Synapses Based on FPGA. Chinese Journal of Biomedical Engineering, 2020, 39(1): 57-66.
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