Spike-based Neuromorphic Computing for Next-Generation Computer Vision

TL;DR

This paper discusses spike-based neuromorphic computing as an energy-efficient alternative for computer vision, emphasizing its potential to emulate brain functions across various design layers and its promising future applications.

Contribution

It provides an overview of neuromorphic computing across multiple abstraction layers and highlights its potential for energy-efficient computer vision applications.

Findings

Neuromorphic computing offers significant energy efficiency improvements.

Spike-based approaches can replace deep neural networks in vision tasks.

Future research directions include applications in edge devices.

Abstract

Neuromorphic Computing promises orders of magnitude improvement in energy efficiency compared to traditional von Neumann computing paradigm. The goal is to develop an adaptive, fault-tolerant, low-footprint, fast, low-energy intelligent system by learning and emulating brain functionality which can be realized through innovation in different abstraction layers including material, device, circuit, architecture and algorithm. As the energy consumption in complex vision tasks keep increasing exponentially due to larger data set and resource-constrained edge devices become increasingly ubiquitous, spike-based neuromorphic computing approaches can be viable alternative to deep convolutional neural network that is dominating the vision field today. In this book chapter, we introduce neuromorphic computing, outline a few representative examples from different layers of the design stack (devices, circuits and algorithms) and conclude with a few exciting applications and future research directions that seem promising for computer vision in the near future.