Hardware specialization of machine-learning kernels: Possibilities for applications and possibilities for the platform design space (invited)

Kyong Ho Lee, Zhuo Wang, Naveen Verma

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Scopus citations

Abstract

This paper considers two challenging trends affecting low-power sensing systems: (1) the applications of interest increasingly involve embedded signals that are very complex to analyze; and (2) the platforms themselves face elevating constraints in terms of energy and possibly cost. Motivated by the complexities of analyzing the application signals, we emphasize the benefits of data-driven approaches. Most notably, these approaches are based on machine learning, as opposed to traditional DSP. We consider how the algorithms lend themselves to specialized signal-analysis platforms. Hardware specialization is well regarded as an approach to address issues of computational efficiency, performance, and capacity, thus playing a key role in leveraging Moore's Law. However, we describe how hardware specialization of machine-learning kernels, this time with an explicit focus on error resilience, can also play a powerful role in enabling system-wide fault tolerance, thereby aiding Moore's Law on another dimension.

Original languageEnglish (US)
Title of host publication2013 IEEE Workshop on Signal Processing Systems, SiPS 2013
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages330-335
Number of pages6
ISBN (Print)9781467362382
StatePublished - Jan 1 2013
Event2013 IEEE Workshop on Signal Processing Systems, SiPS 2013 - Taipei, Taiwan, Province of China
Duration: Oct 16 2013Oct 18 2013

Publication series

NameIEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation
ISSN (Print)1520-6130

Other

Other2013 IEEE Workshop on Signal Processing Systems, SiPS 2013
CountryTaiwan, Province of China
CityTaipei
Period10/16/1310/18/13

All Science Journal Classification (ASJC) codes

  • Electrical and Electronic Engineering
  • Signal Processing
  • Applied Mathematics
  • Hardware and Architecture

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