TY  - JOUR
UR  - http://dx.doi.org/10.1109/TCSVT.2014.2321071
ID  - discovery1452450
N2  - 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
Y1  - 2014/11//
A1  - Anam, MA
A1  - Whatmough, PN
A1  - Andreopoulos, Y
JF  - IEEE Transactions on Circuits and Systems for Video Technology
SP  - 1860
VL  - 24
EP  -  1873
IS  - 11
AV  - public
SN  - 1051-8215
TI  - Precision-energy-throughput scaling of generic matrix multiplication and convolution kernels via linear projections
KW  - Convolution (CONV)
KW  -  
Embedded systems
KW  - 
Energy and throughput scaling
KW  -  
Generic matrix multiplication
(GEMM)
KW  -  Multimedia recognition and matching
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ER  -