Edge ML vs. TinyML: A Comparative Analysis with Experimental Results on Power Efficiency and Model Optimization

The deployment of machine learning (ML) on edge devices and ultra-low-power microcontrollers (MCUs) has led to the emergence of two dominant paradigms: Edge ML and TinyML. Edge ML leverages moderately powerful devices—such as embedded systems and AI-capable computing platforms—for real-time inference. In contrast, TinyML pushes the boundaries by enabling ML capabilities on ultra-low-power microcontrollers operating at milliwatt-level power consumption. This paper presents a comprehensive comparative analysis of these two approaches, supported by experimental results from an object detection application (egg counting) implemented on both an MCU-based device (Arduino Nicla Vision) using TinyML and a single-board unit (Raspberry Pi) employing Edge ML. We evaluate key metrics, including power consumption, latency, and the trade-offs between INT8 and FLOAT32 model representations. Our findings show that TinyML achieves significantly lower energy consumption than Edge ML for comparable tasks, positioning it as a more suitable choice for battery-operated IoT applications.