• DOI:10.1109/HPCA47549.2020.00011
Corpus ID: 208154666

Deep Learning Acceleration with Neuron-to-Memory Transformation

@article{Imani2020DeepLA,
  title={Deep Learning Acceleration with Neuron-to-Memory Transformation},
  author={M. Imani and Mohammad Samragh Razlighi and Y. Kim and Saransh Gupta and F. Koushanfar and T. Simunic},
  journal={2020 IEEE International Symposium on High Performance Computer Architecture (HPCA)},
  year={2020},
  pages={1-14}
}

• M. Imani, Mohammad Samragh Razlighi, +3 authors T. Simunic
• Published 2020
• Computer Science
• 2020 IEEE International Symposium on High Performance Computer Architecture (HPCA)

Deep neural networks (DNN) have demonstrated effectiveness for various applications such as image processing, video segmentation, and speech recognition. Running state-of-theart DNNs on current systems mostly relies on either generalpurpose processors, ASIC designs, or FPGA accelerators, all of which suffer from data movements due to the limited on-chip memory and data transfer bandwidth. In this work, we propose a novel framework, called RAPIDNN, which performs neuron-to-memory transformation… Expand

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