With the increasing demand for implementing deep-learning models on devices on resource-constrained devices, the development of power-efficient neural networks has become imperative. This paper introduces HADQ-Net, a novel framework for optimizing deep convolutional neural networks (CNNs) through Quantization-Aware Training (QAT). By compressing 32-bit floating-point (FP32) precision weights and activation values to lower bit-widths, HADQ-Net significantly reduces memory footprint and computational complexity while maintaining high accuracy. We propose adaptive quantization limits based on the statistical properties of each layer or channel, coupled with normalization techniques, to enhance quantization efficiency and accuracy. The framework includes algorithms for QAT, quantized convolution, and quantized inference, enabling efficient deployment of deep CNN models on edge devices. Extensive experiments across tasks such as super-resolution, classification, object detection, and semantic segmentation demonstrate the trade-offs between accuracy, model size, and computational efficiency under various quantization levels. Our results highlight the superiority of QAT over post-training quantization methods and underscore the impact of quantization types on model performance. HADQ-Net achieves significant reductions in memory footprint, computational complexity, and energy consumption, making it ideal for resource-constrained environments without sacrificing performance.
Oflamaz et al. (2025) studied this question.