An affordable hardware-aware neural architecture search for deploying convolutional neural networks on ultra-low-power computing platforms

2026-06-15

AI Summary

This work addresses the limitations of existing hardware-aware neural architecture search (HW-NAS) methods, which are primarily tailored for high-performance microcontrollers and fail to meet the stringent resource constraints of ultra-low-power sensing nodes. To bridge this gap, the authors propose a lightweight HW-NAS framework specifically designed for ultra-low-power microcontrollers, enabling, for the first time, fully on-device, end-to-end searchable deployment of tiny convolutional neural networks directly on embedded platforms. By jointly optimizing model accuracy and hardware-specific constraints, the method demonstrates consistent effectiveness across three mainstream micro-vision benchmarks. The resulting architectures achieve state-of-the-art classification accuracy while being successfully deployable on ultra-low-power hardware, thereby advancing the feasibility of intelligent edge inference under extreme energy budgets.

Abstract

Hardware-aware neural architecture search (HW-NAS) allows the integration of Convolutional Neural Networks (CNNs) in microcontrollers devices by automatically designing neural architectures that can fit prearranged hardware constraints. However, state-of-the-art HW-NAS target high-performance microcontrollers, whose power consumption does not meet sensing nodes requirements. This work presents a HW-NAS generating tiny CNNs that can run on ultra-low-power microcontrollers, featuring a lightweight search procedure enabling its execution even on embedded devices. Empirical results on three well-known benchmarks for tiny computer vision proved that the proposed HW-NAS was able to generate tiny CNNs while preserving state-of-the-art classification accuracy.

Problem

• hardware-aware neural architecture search
• ultra-low-power microcontrollers
• convolutional neural networks
• tiny CNNs
• embedded devices

Innovation

• hardware-aware NAS
• ultra-low-power microcontrollers
• tiny CNNs
• lightweight search
• embedded deployment