%0 Journal Article
%@ 1051-8215
%A Anam, MA
%A Whatmough, PN
%A Andreopoulos, Y
%D 2014
%F discovery:1452450
%J IEEE Transactions on Circuits and Systems for Video Technology
%K Convolution (CONV),   Embedded systems,  Energy and throughput scaling,   Generic matrix multiplication  (GEMM), Multimedia recognition and matching
%N 11
%P 1860- 1873
%T Precision-energy-throughput scaling of generic matrix multiplication and convolution kernels via linear projections
%U https://discovery.ucl.ac.uk/id/eprint/1452450/
%V 24
%X 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
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