YodaNN: An Architecture for Ultralow Power Binary-Weight CNN Acceleration

@article{Andri2018YodaNNAA,
  title={YodaNN: An Architecture for Ultralow Power Binary-Weight CNN Acceleration},
  author={R. Andri and L. Cavigelli and D. Rossi and L. Benini},
  journal={IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems},
  year={2018},
  volume={37},
  pages={48-60}
}
  • R. Andri, L. Cavigelli, +1 author L. Benini
  • Published 2018
  • Computer Science
  • IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems
Convolutional neural networks (CNNs) have revolutionized the world of computer vision over the last few years, pushing image classification beyond human accuracy. The computational effort of today’s CNNs requires power-hungry parallel processors or GP-GPUs. Recent developments in CNN accelerators for system-on-chip integration have reduced energy consumption significantly. Unfortunately, even these highly optimized devices are above the power envelope imposed by mobile and deeply embedded… Expand
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