High-Throughput Training of Deep CNNs on ReRAM-Based Heterogeneous Architectures via Optimized Normalization Layers
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DOI: 10.1109/tcad.2021.3083684
Abstract: Resistive random-access memory (ReRAM)-based architectures can be used to accelerate convolutional neural network (CNN) training. However, existing architectures either do not support normalization at all or they support only a limited version of it. Moreover,… read more here.
Keywords: deep cnns; methodology; reram based; normalization layers ... See more keywords