Multilayer Perceptron Algorithm: Impact of Nonideal Conductance and Area-Efficient Peripheral Circuits

@inproceedings{Sanches2017MultilayerPA,
  title={Multilayer Perceptron Algorithm: Impact of Nonideal Conductance and Area-Efficient Peripheral Circuits},
  author={Lucas L. Sanches and Alessandro Fumarola and Severin Sidler and Pritish Narayanan and Irem Boybat and Junwoo Jang and Kibong Moon and Robert M. Shelby and Yusuf Leblebici and Hyunsang Hwang and Geoffrey W. Burr},
  year={2017}
}
Large arrays of the same nonvolatile memories (NVMs) being developed for storage-class memory (SCM) – such as phase-change memory (PCM) and resistive RAM (RRAM) – can also be used in non-Von Neumann neuromorphic computational schemes, with device conductance serving as synaptic “weight.” This allows the all-important multiply-accumulate operation within these algorithms to be performed efficiently at the weight data. 

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