Standard Deviation-Based Quantization for Deep Neural Networks

• URL: http://arxiv.org/abs/2202.12422v1
• Date: Thu, 24 Feb 2022 23:33:47 GMT
• Title: Standard Deviation-Based Quantization for Deep Neural Networks
• Authors: Amir Ardakani, Arash Ardakani, Brett Meyer, James J. Clark, Warren J. Gross
• Abstract summary: Quantization of deep neural networks is a promising approach that reduces the inference cost. We propose a new framework to learn the quantization intervals (discrete values) using the knowledge of the network's weight and activation distributions. Our scheme simultaneously prunes the network's parameters and allows us to flexibly adjust the pruning ratio during the quantization process.
• Score: 17.495852096822894
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantization of deep neural networks is a promising approach that reduces the inference cost, making it feasible to run deep networks on resource-restricted devices. Inspired by existing methods, we propose a new framework to learn the quantization intervals (discrete values) using the knowledge of the network's weight and activation distributions, i.e., standard deviation. Furthermore, we propose a novel base-2 logarithmic quantization scheme to quantize weights to power-of-two discrete values. Our proposed scheme allows us to replace resource-hungry high-precision multipliers with simple shift-add operations. According to our evaluations, our method outperforms existing work on CIFAR10 and ImageNet datasets and even achieves better accuracy performance with 3-bit weights and activations when compared to the full-precision models. Moreover, our scheme simultaneously prunes the network's parameters and allows us to flexibly adjust the pruning ratio during the quantization process.

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