Mohammad Ashraful Anam

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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(More)
—A new roll-forward technique is proposed that recovers from any single fail-stop failure in M integer data streams (M ≥ 3) when undergoing linear, sesquilinear or bijec-tive (LSB) operations, such as: scaling, additions/subtractions, inner or outer vector products and permutations. In the proposed approach, the M input integer data streams are linearly(More)
—Computation-as-a-Service (CaaS) offerings have gained traction in the last few years due to their effectiveness in balancing between the scalability of Software-as-a-Service and the customisation possibilities of Infrastructure-as-a-Service platforms. To function effectively, a CaaS platform must have three key properties: (i) reactive assignment of(More)
—A new technique is proposed for fault-tolerant linear, sesquilinear and bijective (LSB) operations on M integer data streams (M ≥ 3), such as: scaling, additions/subtractions, inner or outer vector products, permutations and convolutions. In the proposed method, the M input integer data streams are linearly superimposed to form M numerically-entangled(More)
—We present Dithen, a novel computation-as-a-service (CaaS) cloud platform specifically tailored to the parallel execution of large-scale multimedia tasks. Dithen handles the upload/download of both multimedia data and executable items, the assignment of compute units to multimedia workloads, and the reactive control of the available compute units to(More)
—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(More)
—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(More)
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