ISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbars

@article{Shafiee2016ISAACAC,
  title={ISAAC: A Convolutional Neural Network Accelerator with In-Situ Analog Arithmetic in Crossbars},
  author={Ali Shafiee and Anirban Nag and Naveen Muralimanohar and Rajeev Balasubramonian and John Paul Strachan and Miao Hu and R. Stanley Williams and Vivek Srikumar},
  journal={2016 ACM/IEEE 43rd Annual International Symposium on Computer Architecture (ISCA)},
  year={2016},
  pages={14-26}
}
A number of recent efforts have attempted to design accelerators for popular machine learning algorithms, such as those involving convolutional and deep neural networks (CNNs and DNNs). These algorithms typically involve a large number of multiply-accumulate (dot-product) operations. A recent project, DaDianNao, adopts a near data processing approach, where a specialized neural functional unit performs all the digital arithmetic operations and receives input weights from adjacent eDRAM banks… 

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