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.

Citation impact

1,335
total citations
FWCI
85.02
Percentile
100%
References
54
Citations per year

Authors

7

Topics & keywords

Keywords
  • Computer science
  • Convolutional neural network
  • Scope (computer science)
  • Deep learning
  • Range (aeronautics)
  • Artificial intelligence
  • Set (abstract data type)
  • Deep neural networks
UN Sustainable Development Goals
  • Affordable and clean energy
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