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Hardware implementation of spiking neural networks on FPGA
2020
Tsinghua Science and Technology
Inspired by real biological neural models, Spiking Neural Networks (SNNs) process information with discrete spikes and show great potential for building low-power neural network systems. This paper proposes a hardware implementation of SNN based on Field-Programmable Gate Arrays (FPGA). It features a hybrid updating algorithm, which combines the advantages of existing algorithms to simplify hardware design and improve performance. The proposed design supports up to 16 384 neurons and 16.8
doi:10.26599/tst.2019.9010019
fatcat:daag6qtcrfh2xklrtbj3nxlmda