SUQ-3: A Three Stage Coarse-to-Fine Compression Framework for Sustainable Edge AI in Smart Farming

Le résumé fourni par la source

Artificial intelligence of things (AIoT) has become a pivotal enabler of precision agriculture by supporting real-time, data-driven decision-making at the edge. Deep learning (DL) models are central to this paradigm, offering powerful capabilities for analyzing environmental and climatic data in a range of agricultural applications. However, deploying these models on edge devices remains challenging due to constraints in memory, computation, and energy. Existing model compression techniques predominantly target large-scale 2D architectures, with limited attention to one-dimensional (1D) models such as gated recurrent units (GRUs), which are commonly employed for processing sequential sensor data. To address this gap, we propose a novel three-stage coarse-to-fine compression framework, termed SUQ-3 (Structured, Unstructured Pruning, and Quantization), designed to optimize 1D DL models for efficient edge deployment in AIoT applications. The SUQ-3 framework sequentially integrates (1) structured pruning with an M×N sparsity pattern to induce hardware-friendly, coarse-grained sparsity; (2) unstructured pruning to eliminate low-magnitude weights for fine-grained compression; and (3) quantization, applied post quantization-aware training (QAT), to support low-precision inference with minimal accuracy loss. We validate the proposed SUQ-3 by compressing a GRU-based crop recommendation model trained on environmental and climatic data from an agricultural dataset. Experimental results show a model size reduction of approximately 85% and an 80% improvement in inference latency while preserving high predictive accuracy (F1 score: 0.97 vs. baseline: 0.9837). Notably, when deployed on a mobile edge device using TensorFlow Lite, the SUQ-3 model achieved an estimated energy consumption of 1.18 μJ per inference, representing a 74.4% reduction compared with the baseline and demonstrating its potential for sustainable low-power AI deployment in agricultural environments. Although demonstrated in an agricultural AIoT use case, the generality and modularity of SUQ-3 make it applicable to a broad range of DL models across domains requiring efficient edge intelligence.

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