IIR Filter-Based Spiking Neural Network

Sai Sanjeet, Rahul K. Meena, Bibhu Datta Sahoo, Keshab K. Parhi, Masahiro Fujita

Abstract: Spiking Neural Networks (SNNs) are closely related to the dynamics of the human brain and use spatiotemporal encoding of information to generate spikes. Implementing various neuronal models in hardware is a popular field of research aiming to mimic biological behavior. The leaky integrate-and-fire model of the neuron is generally chosen for hardware implementation owing to its simplicity and accuracy in modeling the neuron. This paper proposes an infinite impulse response (IIR) filter-based neuron model and describes a backpropagation-based training algorithm for an SNN built using the proposed neurons. The trained network is implemented on an Ultra96-V2 FPGA to validate the design and demonstrate the power and resource efficiency. The implemented design achieves an accuracy of 98.91% on the MNIST dataset and classifies images at 13,021 frames-per-second (FPS) with a 200 MHz clock while consuming < 700 mW of power. The proposed design achieves similar energy efficiency as previous works and <tex>$\approx 7.5\times$</tex> higher resource efficiency than previous publications.