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)
The generic matrix multiply (GEMM) routine comprises the compute and memory-intensive part of many information retrieval, relevance ranking and object recognition systems. Because of the prevalence of GEMM in these applications, ensuring its robustness to transient hardware faults is of paramount importance for highly-efficient/highly-reliable systems. This(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)
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 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)
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, M input integer data streams are linearly superimposed to form M numerically-entangled(More)
The generic matrix multiply (GEMM) routine comprises the compute and memory-intensive part of many information retrieval, machine learning and object recognition systems that process integer inputs. Therefore, it is of paramount importance to ensure that integer GEMM computations remain robust to silent data corruptions (SDCs), which stem from accidental(More)
—We propose a new technique for the mitigation of fail-stop failures and/or silent data corruptions (SDCs) within linear, sesquilinear or bijective (LSB) operations on M integer data streams (M C 3). In the proposed approach, the M input streams are linearly superimposed to form M numerically entangled integer data streams that are stored in-place of the(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)
—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)
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