analiza aiqbench
This survey looks at how machine learning is deployed on microcontrollers and edge devices in precision farming, with an emphasis on the full deployment stack rather than accuracy alone. Quantization dominates the reported optimizations, while pruning and hardware-aware architecture search are less common. Inconsistent reporting of flash, RAM, latency, and energy limits reproducibility, and the paper's observation that inference moves to devices while training stays centralized is a useful framing for TinyML practitioners.
Edge AI and TinyML for Resource-Constrained Agriculture: Hardware Platforms, Model Optimization, and Deployment Strategies
arXiv:2603.15085v2 Announce Type: replace-cross Abstract: Deploying artificial intelligence at the edge is increasingly important for systems that must operate under strict limits on memory, computation, energy, connectivity, and hardware cost. Tiny Machine Learning (TinyML) represents the most resource-constrained regime of this shift, enabling inference on microcontroller-class devices with tightly bounded flash and SRAM budgets. This study examines recent deployment trends in Edge AI and TinyML using precision farming as a representative real-world environment in which sensing nodes, gateways, UAVs, and embedded processors must often operate with intermittent connectivity and limited power. The analysis emphasizes the deployment stack rather than predictive accuracy alone, covering hardware classes, software toolchains, model- and system-level optimization, intelligence placement, and hardware-resource reporting. Across the analyzed studies, microcontroller-class platforms such as ESP32, STM32, and ATmega are prominent endpoint targets, while single-board computers and embedded accelerators support more compute-intensive workloads. Among the studies included in the optimization comparison, almost 50% report quantization, making reduced-precision inference the most frequently identified strategy within this subset. In contrast, structured pruning, hardware-aware neural architecture search, and coordinated cross-layer optimization remain comparatively limited. Resource profiling is also inconsistent as flash, peak RAM, MAC count, latency, and energy per inference are not reported uniformly, limiting reproducibility and comparisons across deployments. A recurring systems-level pattern is an inference-training asymmetry in which inference moves toward local devices while training and model lifecycle management remain predominantly centralized.
Abstract:Deploying artificial intelligence at the edge is increasingly important for systems that must operate under strict limits on memory, computation, energy, connectivity, and hardware cost. Tiny Machine Learning (TinyML) represents the most resource-constrained regime of this shift, enabling inference on microcontroller-class devices with tightly bounded flash and SRAM budgets. This study examines recent deployment trends in Edge AI and TinyML using precision farming as a representative real-world environment in which sensing nodes, gateways, UAVs, and embedded processors must often operate with intermittent connectivity and limited power. The analysis emphasizes the deployment stack rather than predictive accuracy alone, covering hardware classes, software toolchains, model- and system-level optimization, intelligence placement, and hardware-resource reporting. Across the analyzed studies, microcontroller-class platforms such as ESP32, STM32, and ATmega are prominent endpoint targets, while single-board computers and embedded accelerators support more compute-intensive workloads. Among the studies included in the optimization comparison, almost 50% report quantization, making reduced-precision inference the most frequently identified strategy within this subset. In contrast, structured pruning, hardware-aware neural architecture search, and coordinated cross-layer optimization remain comparatively limited. Resource profiling is also inconsistent as flash, peak RAM, MAC count, latency, and energy per inference are not reported uniformly, limiting reproducibility and comparisons across deployments. A recurring systems-level pattern is an inference-training asymmetry in which inference moves toward local devices while training and model lifecycle management remain predominantly centralized.
Emerging Technologies (cs.ET); Machine Learning (cs.LG)
arXiv:2603.15085 [cs.ET]
(or arXiv:2603.15085v2 [cs.ET] for this version)
https://doi.org/10.48550/arXiv.2603.15085 Focus to learn more arXiv-issued DOI via DataCite
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