Generic matrix multiplication (GEMM) and one-dimensional
convolution/cross-correlation (CONV) 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
280%~440% increase of processing throughput and 75%~80% decrease of energy
consumption against optimized GEMM and CONV kernels without any impact in 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
