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}
}
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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