Divide and Conquer: Leveraging Intermediate Feature Representations for Quantized Training of Neural Networks

14 Jun 2019Ahmed T. ElthakebPrannoy PilligundlaHadi Esmaeilzadeh

The deep layers of modern neural networks extract a rather rich set of features as an input propagates through the network. This paper sets out to harvest these rich intermediate representations for quantization with minimal accuracy loss while significantly reducing the memory footprint and compute intensity of the DNN... (read more)

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