[Submitted on 10 Aug 2026]
Title:ENAS: An Efficient Hardware-Aware Neural Architecture Search Framework for TinyML on Resource-Constrained Microcontrollers
View a PDF of the paper titled ENAS: An Efficient Hardware-Aware Neural Architecture Search Framework for TinyML on Resource-Constrained Microcontrollers, by Mohd Moin Khan and Naman Srivastava and Pandarasamy Arjunan
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Abstract:We present \textbf{ENAS}, a hardware-aware Neural Architecture Search (NAS) framework that combines a static feasibility check, a cell-based search space supporting standard, depthwise-separable, and bottleneck blocks with optional skip connections, and a three-stage hybrid search strategy (random $\rightarrow$ top-$K$ $\rightarrow$ mutation) with persistent cross-run caching. Unlike many existing NAS frameworks that rely on GPU acceleration, ENAS is designed to operate efficiently without requiring GPUs, making it suitable for resource-constrained development environments. We evaluate ENAS on two TinyML benchmarks, Visual Wake Words and Melanoma Cancer, across eight microcontrollers with memory footprints ranging from 20\,KB to 1\,MB SRAM and nine input image resolutions. Our experimental results show that ENAS achieves mean search-time speedups of $2.41{\times}$ and $1.70{\times}$ on the Visual Wake Words and Melanoma Cancer datasets, respectively, while maintaining competitive test accuracy compared with the recent NanoNAS framework. A measured resource analysis further shows that ENAS-selected models use substantially lower peak activation RAM, the binding constraint for microcontroller deployment at matched accuracy. Additionally, ENAS achieves $79.4\%$ test accuracy on an STM32H743-based microcontroller, outperforming the greedy CPU-only baseline by $2.6$ percentage points. We release the ENAS framework as open-source at: this https URL
Subjects:
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
ACM classes: I.2
Cite as: arXiv:2609.30272 [cs.LG]
(or arXiv:2609.30272v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.30272
arXiv-issued DOI via DataCite
Submission history
From: Pandarasamy Arjunan [view email] [v1] Mon, 10 Aug 2026 14:13:13 UTC (261 KB)
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