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FlatENN: Train Flat for Enhanced Fault Tolerance of Quantized Deep Neural Networks
Moreover, we show that SAQ-trained activation-sparse QDNNs show better accuracy in faulty settings than standard QDNNs trained conventionally.
Valley-Spin Hall Effect-based Nonvolatile Memory with Exchange-Coupling-Enabled Electrical Isolation of Read and Write Paths
Valley-spin hall (VSH) effect in monolayer WSe2 has been shown to exhibit highly beneficial features for nonvolatile memory (NVM) design.
TiM-DNN: Ternary in-Memory accelerator for Deep Neural Networks
The use of lower precision has emerged as a popular technique to optimize the compute and storage requirements of complex Deep Neural Networks (DNNs).
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