Papers › Constructing Energy-efficient Mixed-precision Neural Networks through Principal...

Constructing Energy-efficient Mixed-precision Neural Networks through Principal Component Analysis for Edge Intelligence

4 Jun 2019arXiv:1906.01493archive 2025-07-28

Indranil Chakraborty, Deboleena Roy, Isha Garg, Aayush Ankit, Kaushik Roy

The `Internet of Things' has brought increased demand for AI-based edge computing in applications ranging from healthcare monitoring systems to autonomous vehicles. Quantization is a powerful tool to address the growing computational cost of such applications, and yields significant compression over full-precision networks. However, quantization can result in substantial loss of performance for complex image classification tasks. To address this, we propose a Principal Component Analysis (PCA) driven methodology to identify the important layers of a binary network, and design mixed-precision networks. The proposed Hybrid-Net achieves a more than 10% improvement in classification accuracy over binary networks such as XNOR-Net for ResNet and VGG architectures on CIFAR-100 and ImageNet datasets while still achieving up to 94% of the energy-efficiency of XNOR-Nets. This work furthers the feasibility of using highly compressed neural networks for energy-efficient neural computing in edge devices.

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Autonomous Vehicles Dimensionality Reduction Edge-computing General Classification Image Classification Quantization image-classification

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1x1 Convolution Average Pooling Batch Normalization Bottleneck Residual Block Convolution Dense Connections Dropout Global Average Pooling Kaiming Initialization Max Pooling ReLU Residual Block Residual Connection Softmax

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