# Constructing Energy-efficient Mixed-precision Neural Networks through Principal Component Analysis for Edge Intelligence Source: [Constructing Energy-efficient Mixed-precision Neural Networks through Principal Component Analysis for Edge Intelligence]() Collection index: [llms.txt]() Data updated: 2025-10-25T22:04:19.938Z Authors: Indranil Chakraborty, Deboleena Roy, Isha Garg, Aayush Ankit, Kaushik Roy Published: 2019-06-04T15:02:30.000Z ## Abstract 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. ## Recorded benchmark results A research result applies to its stated dataset, split, metric and evaluation configuration. Compare like-for-like settings, preserve the metric's unit and direction, and distinguish paper-reported results from independent reproduction. The best result recorded here is not a claim of exhaustive, current global coverage. ## Original sources and implementation [Original paper]() [PDF]()