Switched by input: Power efficient structure for RRAM-based convolutional neural network

@article{Xia2016SwitchedBI,
  title={Switched by input: Power efficient structure for RRAM-based convolutional neural network},
  author={Lixue Xia and Tianqi Tang and Wenqin Huangfu and Ming Cheng and Xiling Yin and Boxun Li and Yu Wang and Huazhong Yang},
  journal={2016 53nd ACM/EDAC/IEEE Design Automation Conference (DAC)},
  year={2016},
  pages={1-6}
}
Convolutional Neural Network (CNN) is a powerful technique widely used in computer vision area, which also demands much more computations and memory resources than traditional solutions. The emerging metal-oxide resistive random-access memory (RRAM) and RRAM crossbar have shown great potential on neuromorphic applications with high energy efficiency. However, the interfaces between analog RRAM crossbars and digital peripheral functions, namely Analog-to-Digital Converters (ADCs) and Digital-to… CONTINUE READING
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