PipeLayer: A Pipelined ReRAM-Based Accelerator for Deep Learning

@article{Song2017PipeLayerAP,
  title={PipeLayer: A Pipelined ReRAM-Based Accelerator for Deep Learning},
  author={Linghao Song and Xuehai Qian and Hai Li and Yiran Chen},
  journal={2017 IEEE International Symposium on High Performance Computer Architecture (HPCA)},
  year={2017},
  pages={541-552}
}
Convolution neural networks (CNNs) are the heart of deep learning applications. Recent works PRIME [1] and ISAAC [2] demonstrated the promise of using resistive random access memory (ReRAM) to perform neural computations in memory. We found that training cannot be efficiently supported with the current schemes. First, they do not consider weight update and complex data dependency in training procedure. Second, ISAAC attempts to increase system throughput with a very deep pipeline. It is only… CONTINUE READING
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