articleIEEE Journal of Solid-State CircuitsNov 8, 2016Closed access

Eyeriss: An Energy-Efficient Reconfigurable Accelerator for Deep Convolutional Neural Networks

Massachusetts Institute of Technology · Georgia Institute of Technology · +1 more institution

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Abstract

Eyeriss is an accelerator for state-of-the-art deep convolutional neural networks (CNNs). It optimizes for the energy efficiency of the entire system, including the accelerator chip and off-chip DRAM, for various CNN shapes by reconfiguring the architecture. CNNs are widely used in modern AI systems but also bring challenges on throughput and energy efficiency to the underlying hardware. This is because its computation requires a large amount of data, creating significant data movement from on-chip and off-chip that is more energy-consuming than computation. Minimizing data movement energy cost for any CNN shape, therefore, is the key to high throughput and energy efficiency. Eyeriss achieves these goals by…

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Authors

4

Topics & keywords

Keywords
  • Dram
  • Computer science
  • Dataflow
  • Convolutional neural network
  • Efficient energy use
  • Computation
  • Parallel computing
  • Throughput
UN Sustainable Development Goals
  • Affordable and clean energy
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