ENAS is a hardware‑aware neural architecture search framework tailored for TinyML on microcontrollers. It uses a static feasibility check, a cell‑based search space with various block types and skip connections, and a three‑stage hybrid search strategy (random → top‑K → mutation) with cross‑run caching. The framework runs efficiently without GPUs, achieving significant search‑time speedups and competitive accuracy on Visual Wake Words and Melanoma Cancer benchmarks across a range of microcontrollers.
By Mohd Moin Khan, Naman Srivastava, Pandarasamy Arjunan
arXiv:2606. 17249v1 Announce Type: cross Abstract: The dominant trajectory of modern machine learning has been to scale up: larger models, larger accelerators, larger memory budgets.
By Emre Can Kizilates
arXiv:2603. 15106v2 Announce Type: replace Abstract: Enabling efficient deep neural network (DNN) inference on edge devices with different hardware constraints is a challenging task that typically requires DNN architectures to be specialized for each device separately.
By Mark Deutel, Simon Geis, Axel Plinge
arXiv:2605. 16138v3 Announce Type: replace Abstract: Neural architecture search (NAS) is a powerful approach for automating model design, but existing methods often optimize for accuracy alone or rely on proxy metrics such as bit operations (BOPs) that correlate poorly with hardware cost.
By Jason Weitz, Dmitri Demler, Benjamin Hawks, Aaron Wang, Nhan Tran, Javier Duarte
arXiv:2607. 11746v1 Announce Type: new Abstract: With deep neural networks (DNNs) increasingly deployed on edge devices, hardware (HW)-aware optimization techniques--such as HW-aware compression and HW-aware neural architecture search (HW-NAS)--have become essential.
By Shambhavi Balamuthu Sampath, Behzad Shomali, Nael Fasfous, Moritz Thoma, Judeson Anthony Fernando, Lukas Frickenstein, Pierpaolo Mori, Manoj Rohit Vemparala, Alexander Frickenstein, Walter Stechele
arXiv:2512.04705v3 Announce Type: replace-cross
Abstract: The deployment of Early-Exiting Neural Networks (EENNs) on edge accelerators requires optimizing not only the network architecture but also i...
By Alaa Zniber, Arne Symons, Ouassim Karrakchou, Marian Verhelst, Mounir Ghogho