Quantized Context Based LIF Neurons for Recurrent Spiking Neural Networks in 45nm

TL;DR

This paper introduces a hardware-implemented, quantized version of context-based LIF neurons for recurrent spiking neural networks, achieving high accuracy and scalability in 45nm technology.

Contribution

It presents the first hardware design of a quantized CLIF neuron model integrated into RSNNs, demonstrating high accuracy and scalability in a compact form.

Findings

Achieved 90% accuracy on DVS gesture dataset with 8-bit quantization.

Implemented a scalable network supporting up to 82k synapses.

Designed a compact neuron model in 45nm technology.

Abstract

In this study, we propose the first hardware implementation of a context-based recurrent spiking neural network (RSNN) emphasizing on integrating dual information streams within the neocortical pyramidal neurons specifically Context- Dependent Leaky Integrate and Fire (CLIF) neuron models, essential element in RSNN. We present a quantized version of the CLIF neuron (qCLIF), developed through a hardware-software codesign approach utilizing the sparse activity of RSNN. Implemented in a 45nm technology node, the qCLIF is compact (900um^2) and achieves a high accuracy of 90% despite 8 bit quantization on DVS gesture classification dataset. Our analysis spans a network configuration from 10 to 200 qCLIF neurons, supporting up to 82k synapses within a 1.86 mm^2 footprint, demonstrating scalability and efficiency