Efficient Neuron Architecture for FPGA-based Spiking Neural Networks

Scalability is a key challenge for digital spiking neural networks (SNN) in hardware. This paper proposes an efficient neuron architecture (ENA) to reduce the silicon area occupied by neurons. As the computation resource (e.g.... [ view full abstract ]

Scalability is a key challenge for digital spiking neural networks (SNN) in hardware. This paper proposes an efficient neuron architecture (ENA) to reduce the silicon area occupied by neurons. As the computation resource (e.g. DSP in FPGAs) is limited for hardware SNNs, the proposed ENA employs a sharing mechanism of computing component at two levels (synapse and neuron) to reduce the occupied resources. The neuron computing core is developed as the key component for the neuron model computation, which is shared by multiple synapses within one neuron cell; and also the computing component of one neuron is shared by several neurons within one layer of the SNN system. A test bench experiment is designed for a Xilinx FPGA device and the results demonstrate that the proposed ENA occupies relatively low hardware resources and has the capability to scale for large SNN implementations.