Evaluating the Energy Efficiency of NPU-Accelerated Machine Learning Inference on Embedded Microcontrollers

The deployment of machine learning (ML) models on microcontrollers (MCUs) is constrained by strict energy, latency, and memory requirements, particularly in battery-operated and real-time edge devices. While software-level optimizations such as quantization and pruning reduce model size and computation, hardware acceleration has emerged as a decisive enabler for efficient embedded inference. This paper evaluates the impact of Neural Processing Units (NPUs) on MCU-based ML execution, using the ARM Cortex-M55 core combined with the Ethos-U55 NPU on the Alif Semiconductor Ensemble E7 development board as a representative platform. A rigorous measurement methodology was employed, incorporating per-inference net energy accounting via GPIO-triggered high-resolution digital multimeter synchronization and idle-state subtraction, ensuring accurate attribution of energy costs. Experimental results across six representative ML models -including MiniResNet, MobileNetV2, FD-MobileNet, MNIST, TinyYolo, and SSD-MobileNet-demonstrate substantial efficiency gains when inference is offloaded to the NPU. For moderate to large networks, latency improvements ranged from 7x to over 125x, with per-inference net energy reductions up to 143x. Notably, the NPU enabled execution of models unsupported on CPU-only paths, such as SSD-MobileNet, highlighting its functional as well as efficiency advantages. These findings establish NPUs as a cornerstone of energy-aware embedded AI, enabling real-time, power-constrained ML inference at the MCU level.