Structured Nonuniform Pruning for Tiny Angle-of-Arrival Deep Learning Models

The deployment of deep learning (DL) models on Internet of Things (IoT) devices is constrained by limited computational resources, necessitating effective model compression techniques. This paper introduces a novel, structured, nonuniform, block-wise compression approach, tailored for resourceconstrained IoT applications that require Angle of Arrival (AoA) estimation. Our method prunes all model blocks of layers, adjusting the pruning intensity based on each block's measured contribution to overall performance: less critical blocks undergo more aggressive pruning, while essential blocks are preserved to maintain accuracy. Applied to MobileNetV3 for AoA estimation, our technique achieved a 35.9X reduction in parameter count, an 11.5X reduction in model size, a 7.7X reduction in multiplyaccumulate operations (MACs), a 4.7X improvement in inference speed on CPU, and a 2.4X improvement on GPU, with minimal impact on accuracy. These results are achieved on a real-world dataset collected on a software defined radio (SDR) testbed to validate the effectiveness of the proposed solution. This demonstrates that our approach can achieve a highly favorable trade-off between compression and performance, supporting the deployment of tiny AoA models in latency-critical and powersensitive IoT environments.