[2603.04134] InstMeter: An Instruction-Level Method to Predict Energy and Latency of DL Model Inference on MCUs

Computer Science > Machine Learning arXiv:2603.04134 (cs) [Submitted on 4 Mar 2026] Title:InstMeter: An Instruction-Level Method to Predict Energy and Latency of DL Model Inference on MCUs Authors:Hao Liu, Qing Wang, Marco Zuniga View a PDF of the paper titled InstMeter: An Instruction-Level Method to Predict Energy and Latency of DL Model Inference on MCUs, by Hao Liu and 2 other authors View PDF Abstract:Deep learning (DL) models can now run on microcontrollers (MCUs). Through neural architecture search (NAS), we can search DL models that meet the constraints of MCUs. Among various constraints, energy and latency costs of the model inference are critical metrics. To predict them, existing research relies on coarse proxies such as multiply-accumulations (MACs) and model's input parameters, often resulting in inaccurate predictions or requiring extensive data collection. In this paper, we propose InstMeter, a predictor leveraging MCUs' clock cycles to accurately estimate the energy and latency of DL models. Clock cycles are fundamental metrics reflecting MCU operations, directly determining energy and latency costs. Furthermore, a unique property of our predictor is its strong linearity, allowing it to be simple and accurate. We thoroughly evaluate InstMeter under different scenarios, MCUs, and software settings. Compared with state-of-the-art studies, InstMeter can reduce the energy and latency prediction errors by $3\times$ and $6.5\times$, respectively, while requiring ...