Low-Power Neuromorphic Hardware for Signal Processing Applications: A review of architectural and system-level design approaches

Document Type

Article

Publication Date

11-1-2019

Abstract

Machine learning has emerged as the dominant tool for implementing complex cognitive tasks that require supervised, unsupervised, and reinforcement learning. While the resulting machines have demonstrated in some cases even superhuman performance, their energy consumption has often proved to be prohibitive in the absence of costly supercomputers. Most state-of-the-art machine-learning solutions are based on memoryless models of neurons. This is unlike the neurons in the human brain that encode and process information using temporal information in spike events. The different computing principles underlying biological neurons and how they combine together to efficiently process information is believed to be a key factor behind their superior efficiency compared to current machine-learning systems.

Identifier

85074460168 (Scopus)

Publication Title

IEEE Signal Processing Magazine

External Full Text Location

https://doi.org/10.1109/MSP.2019.2933719

e-ISSN

15580792

ISSN

10535888

First Page

97

Last Page

110

Issue

6

Volume

36

Grant

H2020

Fund Ref

Semiconductor Research Corporation

This document is currently not available here.

Share

COinS