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Memory Requirement Reduction of Deep Neural Networks Using Low-bit Quantization of Parameters [article]

Niccoló Nicodemo and Gaurav Naithani and Konstantinos Drossos and Tuomas Virtanen and Roberto Saletti
2019 arXiv   pre-print

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Effective employment of deep neural networks (DNNs) in mobile devices and embedded systems is hampered by requirements for memory and computational power. This paper presents a non-uniform quantization approach which allows for dynamic quantization of DNN parameters for different layers and within the same layer. A virtual bit shift (VBS) scheme is also proposed to improve the accuracy of the proposed scheme. Our method reduces the memory requirements, preserving the performance of the network.
more » ... The performance of our method is validated in a speech enhancement application, where a fully connected DNN is used to predict the clean speech spectrum from the input noisy speech spectrum. A DNN is optimized and its memory footprint and performance are evaluated using the short-time objective intelligibility, STOI, metric. The application of the low-bit quantization allows a 50% reduction of the DNN memory footprint while the STOI performance drops only by 2.7%.
arXiv:1911.00527v1 fatcat:vvrqusiyirft7j6qrfn677priy
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Niccoló Nicodemo and Gaurav Naithani and Konstantinos Drossos and Tuomas Virtanen and Roberto Saletti. "Memory Requirement Reduction of Deep Neural Networks Using Low-bit Quantization of Parameters." arXiv (2019)