Highly efficient neuromorphic learning system of spiking neural network with multi-compartment leaky integrate-and-fire neurons - Gao, Tian; Deng, Bin; Wang, Jiang; Yi, Guosheng (Frontiers in Neuroscience, 2022)
Spiking neural network (SNN) is considered as the high-performance learning system, which matches the digital circuits and presents higher efficiency due to the architecture and computation of spiking neurons. While implementing a SNN on field-programmable gate array (FPGA), the gradient backpropagation through layers consumes a surprising number of resources. In this paper, we aim to realize an efficient architecture of SNN on FPGA to reduce the resource and power consumption. The multi-compartment leaky integrate-and-fire (MLIF) model is used to convert spike trains to the plateau potential in dendrites. We accumulate the potential in the apical dendrite during the training period. The average of this accumulative result is the dendritic plateau potential and used to guide the updates of synaptic weights. Based on this architecture, the SNN is implemented on FPGA efficiently. In the implementation of neuromorphic learning system, Shift multiplier (Shift MUL) module and piecewise linear (PWL) algorithm are used to replace multipliers and complex nonlinear functions to match the digital circuits. The neuromorphic learning system is constructed with resources on FPGA without dataflow between on-chip and off-chip memories. Compared with previous on-chip learning systems, our neuromorphic learning system performs higher resource utilization and power efficiency.