7,492 research outputs found
Throughput Scaling Of Convolution For Error-Tolerant Multimedia Applications
Convolution and cross-correlation are the basis of filtering and pattern or
template matching in multimedia signal processing. We propose two throughput
scaling options for any one-dimensional convolution kernel in programmable
processors by adjusting the imprecision (distortion) of computation. Our
approach is based on scalar quantization, followed by two forms of tight
packing in floating-point (one of which is proposed in this paper) that allow
for concurrent calculation of multiple results. We illustrate how our approach
can operate as an optional pre- and post-processing layer for off-the-shelf
optimized convolution routines. This is useful for multimedia applications that
are tolerant to processing imprecision and for cases where the input signals
are inherently noisy (error tolerant multimedia applications). Indicative
experimental results with a digital music matching system and an MPEG-7 audio
descriptor system demonstrate that the proposed approach offers up to 175%
increase in processing throughput against optimized (full-precision)
convolution with virtually no effect in the accuracy of the results. Based on
marginal statistics of the input data, it is also shown how the throughput and
distortion can be adjusted per input block of samples under constraints on the
signal-to-noise ratio against the full-precision convolution.Comment: IEEE Trans. on Multimedia, 201
Throughput-Distortion Computation Of Generic Matrix Multiplication: Toward A Computation Channel For Digital Signal Processing Systems
The generic matrix multiply (GEMM) function is the core element of
high-performance linear algebra libraries used in many
computationally-demanding digital signal processing (DSP) systems. We propose
an acceleration technique for GEMM based on dynamically adjusting the
imprecision (distortion) of computation. Our technique employs adaptive scalar
companding and rounding to input matrix blocks followed by two forms of packing
in floating-point that allow for concurrent calculation of multiple results.
Since the adaptive companding process controls the increase of concurrency (via
packing), the increase in processing throughput (and the corresponding increase
in distortion) depends on the input data statistics. To demonstrate this, we
derive the optimal throughput-distortion control framework for GEMM for the
broad class of zero-mean, independent identically distributed, input sources.
Our approach converts matrix multiplication in programmable processors into a
computation channel: when increasing the processing throughput, the output
noise (error) increases due to (i) coarser quantization and (ii) computational
errors caused by exceeding the machine-precision limitations. We show that,
under certain distortion in the GEMM computation, the proposed framework can
significantly surpass 100% of the peak performance of a given processor. The
practical benefits of our proposal are shown in a face recognition system and a
multi-layer perceptron system trained for metadata learning from a large music
feature database.Comment: IEEE Transactions on Signal Processing (vol. 60, 2012
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