eprintid: 1452450
rev_number: 45
eprint_status: archive
userid: 608
dir: disk0/01/45/24/50
datestamp: 2015-03-03 10:57:14
lastmod: 2021-12-16 23:46:33
status_changed: 2015-03-03 11:32:47
type: article
metadata_visibility: show
item_issues_count: 0
creators_name: Anam, MA
creators_name: Whatmough, PN
creators_name: Andreopoulos, Y
title: Precision-energy-throughput scaling of generic matrix multiplication and convolution kernels via linear projections
ispublished: pub
divisions: UCL
divisions: B04
divisions: C05
divisions: F46
keywords: Convolution (CONV), 
Embedded systems,
Energy and throughput scaling, 
Generic matrix multiplication
(GEMM), Multimedia recognition and matching
note: © 2014 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works.
abstract: Generic matrix multiplication (GEMM) and con-
volution (CONV)/cross-correlation kernels often constitute the
bulk of the compute- and memory-intensive processing within
image/audio recognition and matching systems. We propose a
novel method to scale the energy and processing throughput of
GEMM and CONV kernels for such error-tolerant multimedia
applications by adjusting the precision of computation. Our
technique employs linear projections to the input matrix or
signal data during the top-level GEMM and CONV blocking
and reordering. The GEMM and CONV kernel processing then
uses the projected inputs and the results are accumulated to
form the final outputs. Throughput and energy scaling takes
place by changing the number of projections computed by
each kernel, which in turn produces approximate results, i.e.,
changes the precision of the performed computation. Results
derived from a voltage- and frequency-scaled ARM Cortex
A15 processor running face recognition and music-matching
algorithms demonstrate that the proposed approach allows for
a 280%–440% increase of processing throug
hput and a 75%–
80% decrease of energy consumption against the optimized
GEMM and CONV kernels without any impact on the obtained
recognition or matching accuracy. Even higher gains can be
obtained, if one is willing to tolerate some reduction in the
accuracy of the recognition and matching applications
date: 2014-11
official_url: http://dx.doi.org/10.1109/TCSVT.2014.2321071
vfaculties: VENG
oa_status: green
full_text_type: other
primo: open
primo_central: open_green
verified: verified_manual
elements_source: crossref
elements_id: 987051
doi: 10.1109/TCSVT.2014.2321071
lyricists_name: Anam, Mohammad
lyricists_name: Andreopoulos, Ioannis
lyricists_id: MAANA40
lyricists_id: IANDR50
full_text_status: public
publication: IEEE Transactions on Circuits and Systems for Video Technology
volume: 24
number: 11
pagerange: 1860- 1873
issn: 1051-8215
citation:        Anam, MA;    Whatmough, PN;    Andreopoulos, Y;      (2014)    Precision-energy-throughput scaling of generic matrix multiplication and convolution kernels via linear projections.                   IEEE Transactions on Circuits and Systems for Video Technology , 24  (11)   1860- 1873.    10.1109/TCSVT.2014.2321071 <https://doi.org/10.1109/TCSVT.2014.2321071>.       Green open access   
 
document_url: https://discovery.ucl.ac.uk/id/eprint/1452450/3/TCSVT7786.pdf