UCL  IRIS
Institutional Research Information Service
UCL Logo
Please report any queries concerning the funding data grouped in the sections named "Externally Awarded" or "Internally Disbursed" (shown on the profile page) to your Research Finance Administrator. Your can find your Research Finance Administrator at https://www.ucl.ac.uk/finance/research/rs-contacts.php by entering your department
Please report any queries concerning the student data shown on the profile page to:

Email: portico-services@ucl.ac.uk

Help Desk: http://www.ucl.ac.uk/ras/portico/helpdesk
Publication Detail
Precision-energy-throughput scaling of generic matrix multiplication and discrete convolution kernels via linear projections
  • Publication Type:
    Conference
  • Authors:
    Anam MA, Whatmough PN, Andreopoulos Y
  • Publication date:
    01/01/2013
  • Pagination:
    21, 30
  • Published proceedings:
    ESTIMedia 2013 - 11th IEEE Symposium on Embedded Systems for Real-Time Multimedia
  • Status:
    Published
Abstract
Generic matrix multiplication (GEMM) and one-dimensional discrete convolution/cross-correlation (CONV) kernels perform 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 decreasing the number of projections computed by each kernel, which in turn produces approximate results, i.e. lowers the precision of the performed computation. Existing realizations of error-tolerant multimedia applications can opt to utilize a small number of the input projections (typically just one) in order to save energy and processing cycles, while all error-intolerant systems can compute all input projections and obtain full-precision outputs. Results derived from a voltage- and frequency-scaled ARM Cortex A15 processor running face recognition demonstrate that the proposed approach allows for 5-fold to 10-fold increase of processing throughput and more than 80% decrease of energy consumption against optimized GEMM and CONV kernels without any impact in the expected recognition and matching precision. © 2013 IEEE.
Publication data is maintained in RPS. Visit https://rps.ucl.ac.uk
 More search options
UCL Researchers
Author
Dept of Electronic & Electrical Eng
University College London - Gower Street - London - WC1E 6BT Tel:+44 (0)20 7679 2000

© UCL 1999–2011

Search by