Corpus ID: 7137628

INsight: A Neuromorphic Computing System for Evaluation of Large Neural Networks

  title={INsight: A Neuromorphic Computing System for Evaluation of Large Neural Networks},
  author={Jaeyong Chung and T. Shin and Yongshin Kang},
Deep neural networks have been demonstrated impressive results in various cognitive tasks such as object detection and image classification. In order to execute large networks, Von Neumann computers store the large number of weight parameters in external memories, and processing elements are timed-shared, which leads to power-hungry I/O operations and processing bottlenecks. This paper describes a neuromorphic computing system that is designed from the ground up for the energy-efficient… Expand
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Synthesis of activation-parallel convolution structures for neuromorphic architectures
  • S. Kim, Jaeyong Chung
  • Computer Science
  • Design, Automation & Test in Europe Conference & Exhibition (DATE), 2017
  • 2017
An unrolling method that generates parallel structures for the convolutional layers depending on a required level of parallel processing is presented and can improve the performance or reduce the power consumption significantly even without area penalty. Expand
Simplifying deep neural networks for neuromorphic architectures
  • Jaeyong Chung, T. Shin
  • Computer Science
  • 2016 53nd ACM/EDAC/IEEE Design Automation Conference (DAC)
  • 2016
This paper presents two techniques, factorization and pruning, that not only compress the models but also maintain the form of the models for the execution on neuromorphic architectures and proposes a novel method to combine the two techniques. Expand
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