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

Bipin Rajendran, Abu Sebastian, Michael Schmuker, Narayan Srinivasa, Evangelos Eleftheriou

Research output: Contribution to journalArticlepeer-review

11 Scopus citations

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.

Original languageEnglish (US)
Article number8888024
Pages (from-to)97-110
Number of pages14
JournalIEEE Signal Processing Magazine
Volume36
Issue number6
DOIs
StatePublished - Nov 2019

All Science Journal Classification (ASJC) codes

  • Signal Processing
  • Electrical and Electronic Engineering
  • Applied Mathematics

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