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February 24, 20141,344 citations

DianNao

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TCTianshi ChenShenzhen Research Institute of Big DataZDZidong DuChinese Academy of SciencesNSNinghui SunHebei University of Technology

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Abstract

Machine-Learning tasks are becoming pervasive in a broad range of domains, and in a broad range of systems (from embedded systems to data centers). At the same time, a small set of machine-learning algorithms (especially Convolutional and Deep Neural Networks, i.e., CNNs and DNNs) are proving to be state-of-the-art across many applications. As architectures evolve towards heterogeneous multi-cores composed of a mix of cores and accelerators, a machine-learning accelerator can achieve the rare combination of efficiency (due to the small number of target algorithms) and broad application scope.

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Cite This Study

Chen et al. (2014) studied this question.

synapsesocial.com/papers/6a0a564ec72bf9c3ae116b1bhttps://doi.org/10.1145/2541940.2541967
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