Titlebook: Benchmarking, Measuring, and Optimizing; Second BenchCouncil Wanling Gao,Jianfeng Zhan,Dan Stanzione Conference proceedings 2020 Springer

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Imaginary Insect or Mite Infestationsescribed dependencies between different entities as edges. Today, a lot of graph computing systems emerge with massive diverse graph applications deployed, evaluating graph computing systems become a challenge work. Existing graph computing benchmarks are constructed with prevalent graph computing a

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Miscellaneous Vector-Borne Diseasese applications, especially for Edge Computing scenarios, due to its high power consumption and high cost. Thus, researchers and engineers have spent a lot of effort on designing edge-side artificial intelligence (AI) processors recently. Because of different edge-side application requirements, edge

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Infectious Diseases and Arthropodschniques, such as data parallelism, model parallelism, data pipeline, weights pruning and quantization have been proposed to accelerate the inference phase of DL workloads. However, there is still lack of a comparison of these optimization techniques to show their performance difference on dedicated

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Necrotic Arachnidism: Brown Recluse Bites issue. In this paper, we exploit, evaluate and validate the performance of the ResNet101 image classification network on Cambricon with Cambricon Caffe framework, demonstrating the availability and ease of use of this system. Experiments with various operational modes and the processes of model inf

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Infectious Diseases and Arthropodsas attracted the attention of IoT vendors. However, research on the IoT scenario inference framework based on the RISC-V architecture is rare. Popular frame-works such as MXNet, TensorFlow, and Caffe are based on the X86 and ARM architectures, and they are not optimized for the IoT scenarios. We pro

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Infectious Diseases and Nanomedicine Intelligence (AI) applications have been extensively deployed in cloud, edge, mobile and IoT devices due to latest breakthroughs in deep learning algorithms and techniques. Therefore, there is an increasing need for enabling deep learning inference on RISC-V. However, at present mainstream machine le