IIR Filter-Based Spiking Neural Network

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
$\approx 7.5\times$
higher resource efficiency than previous publications.