TY - GEN
T1 - Hardware specialization of machine-learning kernels
T2 - 2013 IEEE Workshop on Signal Processing Systems, SiPS 2013
AU - Lee, Kyong Ho
AU - Wang, Zhuo
AU - Verma, Naveen
PY - 2013
Y1 - 2013
N2 - 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.
AB - 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.
KW - Accelerators
KW - Embedded systems hardware resilience
KW - Machine learning
UR - http://www.scopus.com/inward/record.url?scp=84896485741&partnerID=8YFLogxK
UR - http://www.scopus.com/inward/citedby.url?scp=84896485741&partnerID=8YFLogxK
U2 - 10.1109/sips.2013.6674528
DO - 10.1109/sips.2013.6674528
M3 - Conference contribution
AN - SCOPUS:84896485741
SN - 9781467362382
T3 - IEEE Workshop on Signal Processing Systems, SiPS: Design and Implementation
SP - 330
EP - 335
BT - 2013 IEEE Workshop on Signal Processing Systems, SiPS 2013
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 16 October 2013 through 18 October 2013
ER -