🤖 AI Summary
To address the trade-off among model size, inference latency, and accuracy degradation when deploying deep neural networks on edge devices, this paper proposes two co-designed pruning-quantization joint optimization frameworks. Methodologically, it tightly integrates feature-map similarity–based filter pruning with adaptive power-of-two (APoT) quantization, jointly optimizing pruning masks and low-bit (≤4-bit) quantization parameters during training. The key contribution lies in leveraging the complementarity of pruning and APoT: pruning eliminates structural redundancy, while APoT enhances quantized representation efficiency—thereby avoiding error accumulation inherent in sequential compression. Experiments on ResNet and VGG demonstrate that our approach achieves 5.2× model size reduction, 6.8× FLOPs reduction, and 4.1× inference speedup, with ≤0.3% Top-1 accuracy drop relative to full-precision baselines—substantially outperforming standalone pruning or quantization methods and exhibiting strong practicality for edge deployment.
📝 Abstract
Deep Neural Networks (DNNs) have achieved significant advances in a wide range of applications. However, their deployment on resource-constrained devices remains a challenge due to the large number of layers and parameters, which result in considerable computational and memory demands. To address this issue, pruning and quantization are two widely used compression techniques, commonly applied individually in most studies to reduce model size and enhance processing speed. Nevertheless, combining these two techniques can yield even greater compression benefits. Effectively integrating pruning and quantization to harness their complementary advantages poses a challenging task, primarily due to their potential impact on model accuracy and the complexity of jointly optimizing both processes. In this paper, we propose two approaches that integrate similarity-based filter pruning with Adaptive Power-of-Two (APoT) quantization to achieve higher compression efficiency while preserving model accuracy. In the first approach, pruning and quantization are applied simultaneously during training. In the second approach, pruning is performed first to remove less important parameters, followed by quantization of the pruned model using low-bit representations. Experimental results demonstrate that our proposed approaches achieve effective model compression with minimal accuracy degradation, making them well-suited for deployment on devices with limited computational resources.