Person
Massimo Giordano
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Related publications (2)
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Training fully connected networks with resistive memories: impact of device failures
Giorgio Cristiano, Massimo Giordano, Martina Bodini
Hardware accelerators based on two-terminal non-volatile memories (NVMs) can potentially provide competitive speed and accuracy for the training of fully connected deep neural networks (FC-DNNs), with respect to GPUs and other digital accelerators. We rece ...
2019Perspective on training fully connected networks with resistive memories: Device requirements for multiple conductances of varying significance
Giorgio Cristiano, Massimo Giordano
Novel Deep Neural Network (DNN) accelerators based on crossbar arrays of non-volatile memories (NVMs)-such as Phase-Change Memory or Resistive Memory-can implement multiply-accumulate operations in a highly parallelized fashion. In such systems, computatio ...
2018