Quantized convolutional neural networks through the lens of partial differential equations

• URL: http://arxiv.org/abs/2109.00095v1
• Date: Tue, 31 Aug 2021 22:18:52 GMT
• Title: Quantized convolutional neural networks through the lens of partial differential equations
• Authors: Ido Ben-Yair, Gil Ben Shalom, Moshe Eliasof, Eran Treister
• Abstract summary: Quantization of Convolutional Neural Networks (CNNs) is a common approach to ease the computational burden involved in the deployment of CNNs. In this work, we explore ways to improve quantized CNNs using PDE-based perspective and analysis.
• Score: 6.88204255655161
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Quantization of Convolutional Neural Networks (CNNs) is a common approach to ease the computational burden involved in the deployment of CNNs, especially on low-resource edge devices. However, fixed-point arithmetic is not natural to the type of computations involved in neural networks. In this work, we explore ways to improve quantized CNNs using PDE-based perspective and analysis. First, we harness the total variation (TV) approach to apply edge-aware smoothing to the feature maps throughout the network. This aims to reduce outliers in the distribution of values and promote piece-wise constant maps, which are more suitable for quantization. Secondly, we consider symmetric and stable variants of common CNNs for image classification, and Graph Convolutional Networks (GCNs) for graph node-classification. We demonstrate through several experiments that the property of forward stability preserves the action of a network under different quantization rates. As a result, stable quantized networks behave similarly to their non-quantized counterparts even though they rely on fewer parameters. We also find that at times, stability even aids in improving accuracy. These properties are of particular interest for sensitive, resource-constrained, low-power or real-time applications like autonomous driving.

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