# Catwalk: Unary Top-K for Efficient Ramp-No-Leak Neuron Design for Temporal Neural Networks

## TL;DR

This paper introduces Catwalk, a novel neuron implementation for temporal neural networks that leverages spike sparsity via unary top-k to improve hardware efficiency, reducing area and power consumption.

## Contribution

It proposes a new neuron design using unary top-k to efficiently handle sparse spike volleys, enhancing CMOS implementation of RNL neurons.

## Key findings

- Catwalk reduces area by 1.39x compared to existing neurons.
- Catwalk reduces power consumption by 1.86x.
- The approach exploits spike sparsity for hardware efficiency.

## Abstract

Temporal neural networks (TNNs) are neuromorphic neural networks that utilize bit-serial temporal coding. TNNs are composed of columns, which in turn employ neurons as their building blocks. Each neuron processes volleys of input spikes, modulated by associated synaptic weights, on its dendritic inputs. Recently proposed neuron implementation in CMOS employs a Spike Response Model (SRM) with a ramp-no-leak (RNL) response function and assumes all the inputs can carry spikes. However, in actual spike volleys, only a small subset of the dendritic inputs actually carry spikes in each compute cycle. This form of sparsity can be exploited to achieve better hardware efficiency. In this paper, we propose a Catwalk neuron implementation by relocating spikes in a spike volley as a sorted subset cluster via unary top-k. Such relocation can significantly reduce the cost of the subsequent parallel counter (PC) for accumulating the response functions from the spiking inputs. This can lead to improvements on area and power efficiency in RNL neuron implementation. Place-and-route results show Catwalk is 1.39x and 1.86x better in area and power, respectively, as compared to existing SRM0-RNL neurons.