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

arXiv: https://arxiv.org/abs/1906.01493 · HTML: https://mlsanity.com/paper/1906.01493/

## Key facts

- Cited by 10 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 4 Jun 2019.

## Built on by (most cited first, 10 of 10)

- [AdderNet and its Minimalist Hardware Design for Energy-Efficient Artificial Intelligence](https://mlsanity.com/paper/2101.10015/) (2021)
- [Mixed-Precision Neural Networks: A Survey](https://mlsanity.com/paper/2208.06064/) (2022)
- [A novel approach for Fair Principal Component Analysis based on eigendecomposition](https://mlsanity.com/paper/2208.11362/) (2022)
- [A Plug-in Tiny AI Module for Intelligent and Selective Sensor Data Transmission](https://mlsanity.com/paper/2402.02043/) (2024)
- [Activation Density based Mixed-Precision Quantization for Energy Efficient Neural Networks](https://mlsanity.com/paper/2101.04354/) (2021)
- [Complexity-aware Adaptive Training and Inference for Edge-Cloud Distributed AI Systems](https://mlsanity.com/paper/2109.06440/) (2021)
- [Spatio-Temporal Pruning for Compressed Spiking Large Language Models](https://mlsanity.com/paper/2508.20122/) (2025)
- [QPART: Adaptive Model Quantization and Dynamic Workload Balancing for Accuracy-aware Edge Inference](https://mlsanity.com/paper/2506.23934/) (2025)
- [Where and How to Enhance: Discovering Bit-Width Contribution for Mixed Precision Quantization](https://mlsanity.com/paper/2508.03002/) (2025)
- [Towards Training-free Automatic Proxy Discovery via Large Language Models for Mixed Precision Quantization](https://mlsanity.com/paper/2512.07419/) (2025)

## Abstract (arXiv)

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.