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ENAS: An Efficient Hardware-Aware Neural Architecture Search Framework for TinyML on Resource-Constrained Microcontrollers

arXiv 2609.30272 ↗First posted 10 Aug 2026cs.LG cs.AIcited by 0

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Abstract (arXiv)

We present 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× and 1.70× 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: https://github.com/EdgeIntelligenceLab/ENAS

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References · 17

  1. 2019EfficientNet: Rethinking Model Scaling for Convolutional Neural Networkscited by 6,442
  2. 2018MobileNetV2: Inverted Residuals and Linear Bottleneckscited by 4,795
  3. 2017MobileNets: Efficient Convolutional Neural Networks for Mobile Vision Applicationscited by 3,845
  4. 2018MnasNet: Platform-Aware Neural Architecture Search for Mobilecited by 943
  5. 2019Once-for-All: Train One Network and Specialize it for Efficient Deploymentcited by 787
  6. 2018ProxylessNAS: Direct Neural Architecture Search on Target Task and Hardwarecited by 673
  7. 2018FBNet: Hardware-Aware Efficient ConvNet Design via Differentiable Neural Architecture Searchcited by 485
  8. 2020MCUNet: Tiny Deep Learning on IoT Devicescited by 273
  9. 2021Zero-Cost Proxies for Lightweight NAScited by 199
  10. 2022On-Device Training Under 256KB Memorycited by 160
  11. 2021MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep Learningcited by 89
  12. 2023MicroNAS: Memory and Latency Constrained Hardware-Aware Neural Architecture Search for Time Series Classification on Microcontrollerscited by 8
5 more
  1. 2024MicroNAS: Zero-Shot Neural Architecture Search for MCUscited by 5
  2. 2024MONAS: Efficient Zero-Shot Neural Architecture Search for MCUscited by 4
  3. 2025ELASTIC: Efficient Once For All Iterative Search for Object Detection on Microcontrollerscited by 3
  4. 2026An affordable hardware-aware neural architecture search for deploying convolutional neural networks on ultra-low-power computing platformscited by 2
  5. 2024TinyTNAS: GPU-Free, Time-Bound, Hardware-Aware Neural Architecture Search for TinyML Time Series Classificationcited by 1

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