A Fair Energy Comparison of Spiking and Artificial Neural Networks on a General-Purpose Microcontroller.

Abstract

Energy efficiency is a key constraint for deploying machine learning models on resource-limited internet of things (IoT) and edge devices. Spiking Neural Networks (SNNs) are often proposed as an energy-efficient alternative to conventional Artificial Neural Networks (ANNs) due to their event-driven computation paradigm. However, direct and fair energy comparisons between SNNs and ANNs on inexpensive general-purpose microcontrollers remain limited. In this work, we present a controlled experimental comparison of inference-time energy consumption between an SNN and an ANN with identical multilayer perceptron architectures deployed on an ESP32-C6 microcontroller. Both networks are implemented in C/C++ without relying on optimized inference libraries, use int8 quantization, and are evaluated on the MNIST dataset. Energy consumption is measured directly during inference under identical implementation and measurement conditions to isolate the impact of the computational paradigm. Quantitatively, the ANN requires 11.01 mJ per inference, whereas the SNN consumes 2.31 mJ, 2.34 mJ, and 3.88 mJ for 1, 3, and 10 simulation timesteps, respectively. This corresponds to an approximate reduction in energy consumption of about [Formula: see text] for a single timestep and still about [Formula: see text] even at ten timesteps, demonstrating that the SNN maintains a substantial efficiency advantage under the evaluated conditions. These findings indicate that spiking neural networks can provide significant inference-time energy savings on widely available microcontroller units even without specialized hardware acceleration, while emphasizing the importance of controlled and reproducible comparison conditions.