EdgeVolution: democratizing multi-objective neural architecture search and end-to-end deployment on microcontrollers

Abstract

Edge AI holds great potential for extending the use of artificial neural networks to resource-constrained edge devices, such as microcontrollers. Despite this potential, optimizing and deploying neural networks on these platforms remains challenging due to a lack of tools for hardware-specific adaptation, leading to reproducibility issues and suboptimal performance. To address these challenges, we present EdgeVolution, an end-to-end hardware-in-the-loop platform that facilitates multi-objective optimization, neural architecture selection, and direct deployment onto target hardware. We demonstrate the versatility of EdgeVolution through four application use cases, showcasing its wide-ranging applicability. By offering a generic and adaptable pipeline, EdgeVolution enables the creation and deployment of neural network models tailored to specific datasets, classification tasks, and hardware constraints, thereby improving accessibility, performance, and reproducibility for AI applications on edge devices.