ZHANG Jian, LIU Jia, WAN Xianjie, et al. Design of High Energy Efficient Spiking Neural Network Accelerator for Image Recognition[J]. 2023, 57(1): 211-220.
DOI:
ZHANG Jian, LIU Jia, WAN Xianjie, et al. Design of High Energy Efficient Spiking Neural Network Accelerator for Image Recognition[J]. 2023, 57(1): 211-220.DOI: 10.7652/xjtuxb202301020.
Design of High Energy Efficient Spiking Neural Network Accelerator for Image Recognition
A high energy efficiency spiking neural network(SNN)accelerator is proposed to solve the problems of low-speed and high-power consumption in image recognition application based on the general processor. Firstly
a multi-core parallel structure is designed for hardware acceleration by adopting the concept of high parallel design in neuromorphic computation. Secondly
considering the sparsity of spike data transmission
the one-to-one inter-core transmission mechanism is designed based on event-driven data transmission and processing
which reduces the hardware resources used for communication and improves the data transmission efficiency. Thirdly
a data arrangement scheme is proposed to speed up the access efficiency of membrane in memory. Finally
a circuit structure combining lookups and XOR is designed
which can quickly transform the event vectors into address-event-represent(AER)format. The proposed design is optimized and implemented on the field programmable logic gate array(FPGA)development board. The experimental results show that when the clock frequency is 100 MHz
the energy required to recognize a handwritten digital image is 1.04 mJ
which is only 1/1 453.8 of the serial software program on the 2.2 GHz universal central processing unit(CPU). The proposed accelerator design scheme is suitable for the real scenarios with high real-time requirements and limited energy.
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