Hardware-Aware NAS

NAS: Neural Architecture Search

Hardware-aware neural architecture search studies how to choose model structures that meet accuracy goals while fitting the real limits of devices, from microcontrollers to accelerators. Methods include evolutionary and differentiable search, weight-sharing supernets, and surrogate predictors, often combined with quantization, pruning, or hardware-in-the-loop measurement. Benchmarks such as NAS-Bench and deployments on embedded boards help compare latency, energy, memory, and accuracy across platforms. A central challenge is that proxy estimates and rankings may not predict feasibility on a different device, while measuring every candidate directly makes search costly.

Latest papers

49 papers4 papers in the last four weeks, up 33% on the four weeks before. 0.0% of all new papers.

MOSHO: A Native Discrete Shark-Inspired Metaheuristic for Hardware-Aware Neural Architecture Search
Oct 23, 2026Nazim Abdeerhmane Aouni, Zakaria Soualah Mohammed, Maroua Ogab +7Hardware-Aware NAS Metaheuristic Optimization
Higher National School of Computer Science (ESI), Algiers, Algeria · IBM Research
Efficient Audio-Visual Speech Enhancement via Reinforcement Learning-Based Neural Architecture Search
Oct 8, 2026Khubaib Ahmed, Ahsan Adeel, Nasir Saleem +3Speech Enhancement Neural Architecture Search
University of Stirling, UK · Edinburgh Napier University, UK · University of Technology and Applied Sciences, Oman
RepTC: Representation-Aware Optimization for Efficient Traffic Classification on Edge IoT Devices
Oct 3, 2026Adel Chehade, Edoardo Ragusa, Paolo Gastaldo +1Edge Inference Cost-Aware Inference
Department of Naval, Electrical, Electronic and Telecommunications Engineering (DITEN), University of Genoa, 16126 Genoa, Italy
Coverage You Can Steer: Online Conformal Calibration for RL-Driven Hardware-Aware NAS
Oct 2, 2026Pedro Brandimarte, Nerea Aranjuelo, Marcos Nieto +1Neural Architecture Search Conformal Prediction
Vicomtech Foundation, Basque Research and Technology Alliance (BRTA) Donostia-San Sebasti´an, Spain
FANS: Federated Adaptive Network Search Learning for Heterogeneous Devices
Sep 5, 2026Jiaxin Zhang, Xingwei Wang, Bo Yi +6Federated Learning Heterogeneous Federated Learning
Northeastern University, School of Computer Science and Engineering, Shenyang, Liaoning, China · Shenyang Aerospace University, School of Computer Science, Shenyang, Liaoning, China · TU Wien, Institute of Information Systems Engineering, Vienna, Austria +1
Measurement-Driven Sub-Network Selection for On-Premise Retrieval-Augmented Factory Agents
Sep 2, 2026Vasileios Rizeakos, Georgios Paisios, Alexandros Machairas +2Hardware-Aware NAS Edge Computing
AI lab of enakronIC PC · Aspasias 72, 15561, Cholargos, Greece · Electrical & Computer Engineering +2
H3DNAS: Hardware-Aware ONNX-Native 3D Point Cloud Model Compression
Sep 2, 2026Anchit Mulye, Rhythm Baghel, Sujay Kumar Ingle +1Neural Network Compression Hardware-Aware NAS
Indian Institute of Technology Jodhpur
Node-wise Feature Encoding for Neural Performance Prediction
Aug 28, 2026Matthew Grenier, William Hammer, Andrew Heuer +3Neural Architecture Performance Prediction Energy-Efficient ML
University of South Carolina Department of Computer Science and Engineering · University of South Carolina Department of Mechanical Engineering
Rank Portability Does Not Imply Feasibility Portability: Target-Specific Evaluation of Joint Hardware Constraints
Aug 20, 2026Wesley ShuHardware-Aware NAS Benchmark Design
The Institute of Energetic Paradigm
NAS-Driven Hardware Accelerator Exploration for Edge AI and Quantization Effects on the Pareto Space
Aug 13, 2026Eleftherios Mylonas, Angelos Kouprizas, Michael Birbas +14-Bit Quantization Hardware-Aware NAS
Dept. of ECE University of Patras Patras, Greece
ENAS: An Efficient Hardware-Aware Neural Architecture Search Framework for TinyML on Resource-Constrained Microcontrollers
Aug 10, 2026Mohd Moin Khan, Naman Srivastava, Pandarasamy ArjunanHardware-Aware NAS Neural Architecture Search
Indian Institute of Science (IISc), Bengaluru 560012, India
Design-Time Optimization of Deep Neural Networks for Intermittent Learning on Microcontrollers
Aug 4, 2026Jakob Schubert, Maximilian Kasper, Maximilian Linke +5On-Device Training Energy-Efficient ML
Fraunhofer Institute for Integrated Circuits IIS, Nürnberg, Germany · Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany · Ruhr University Bochum (RUB), Bochum, Germany +2
Filling the Pareto-Optimal Front for Affordance Segmentation on Embedded Devices Using RGB-D Cameras
Jul 30, 2026Edoardo Ragusa, Giovanni Paolo Canuti, Simone Lugani +2Hardware-Aware NAS Edge ML
DITEN, University of Genoa, Genova, Italy
Device-First Feedback: Toward Mobile-Native LLM-Driven Neural Architecture Search
Jul 29, 2026Saif U Din, Muhammad Ahsan Hussain, Radu Timofte +1Large Language Model-Guided NAS Hardware-Aware NAS
Computer Vision Lab, CAIDAS & IFI, University of Würzburg, Germany
OrchNAS: Orchestrated Neural Architecture Search Service for Personalised Federated Edge Intelligence
Jul 24, 2026Keya Patel, Sajib Mistry, Sheik Mohammad Mostakim Fattah +1Energy-Efficient ML Edge Computing
School of EECMS, Curtin University, Australia
HiFi-LLP: High-Fidelity, Low-Cost Latency Predictors with Confidence for Robust HW-NAS
Jul 13, 2026Shambhavi Balamuthu Sampath, Behzad Shomali, Nael Fasfous +7Confidence Estimation Hardware-Aware NAS
Technical University of Munich · BMW Group · University of Bonn
MOSAIC: Adaptive Inter-layer Composition for Efficient Heterogeneous Vision-Language Models
Jul 10, 2026Yuncheng Yang, Feiyang Ye, Shixian Luo +7Vision-Language Models Efficient VLM Inference
LiAuto Inc.
Latency-Constrained DNN Architecture Learning for Edge Systems using Zerorized Batch Normalization
Jul 8, 2026Shuo Huai, Di Liu, Hao Kong +4Efficient Neural Network Inference Edge Inference
aSchool of Computer Science and Engineering, Nanyang Technological University, 639798, Singapore · cDepartment of Computer Science, Norwegian University of Science and Technology, 7491, Trondheim, Norway · bHP-NTU Digital Manufacturing Corporate Lab, Nanyang Technological University, 639798, Singapore +1
LEMUR 2: Unlocking Neural Network Diversity for AI
Jul 7, 2026Tolgay Atinc Uzun, Waleed Khalid, Saif U Din +17Hardware-Aware NAS Neural Architecture Search
Computer Vision Chair, University of Ẅurzburg, Germany
Hardware-aware Graph Neural Networks prunning for embedded event-based vision
Jul 7, 2026Piotr Wzorek, Kamil Jeziorek, Tomasz KryjakEvent-Based Vision Graph Neural Networks
Embedded Perception and Autonomous Systems Group, AGH University of Krakow, Poland
Efficient Network Inference via Hardware-Aware Architecture Search, Model Pruning & Quantization
Jun 22, 2026Lucas Heublein, Mark Deutel, Axel Plinge +1Efficient Neural Network Inference Hardware-Aware NAS
Fraunhofer Institute for Integrated Circuits IIS, 90411 Nürnberg, Germany
A3C3: AI Algorithm and Accelerator Co-design, Co-search, and Co-generation
Jun 18, 2026Selin Yildirim, Yingbing Huang, Deming ChenHardware-Aware NAS Hardware-Software Co-Design
Siebel School of Computing and Data Science · Department of Electrical and Computer Engineering University of Illinois Urbana-Champaign
An affordable hardware-aware neural architecture search for deploying convolutional neural networks on ultra-low-power computing platforms
Jun 15, 2026Andrea Mattia Garavagno, Edoardo Ragusa, Antonio Frisoli +1Efficient Neural Network Inference Tiny ML
Dept. of Electrical, Electronic, Telecommunications Engineering and Naval Architecture (DITEN), University of Genoa, Genoa, Italy · Department of Excellence in Robotics & AI and Institute of Mechanical Intelligence, Scuola Superiore Sant’Anna, 56127 Pisa, Italy
CREST: Deployment-Realistic Hardware-in-the-Loop NAS for Embedded Sensing Systems
Jun 12, 2026Joseph Q. Zales, Pragya Sharma, Mani SrivastavaEnergy-Efficient ML Edge Inference
University of California, Los Angeles · Los Angeles, California, USA
On-Device Neural Architecture Search
Jun 12, 2026Andrea Mattia Garavagno, Edoardo Ragusa, Paolo Gastaldo +2Hardware-Aware NAS Edge ML
Department of Electrical, Electronic, Telecommunication Engineering and Naval Architecture, DITEN University of Genoa, Genoa 16145, Italy · Department of Excellence in Robotics & AI, Scuola Superiore Sant’Anna, Piazza Martiri della Libert`a 33, Pisa 56127, Italy · Institute of Mechanical Intelligence, Scuola Superiore Sant’Anna, Ghezzano, 56010 Pisa, Italy +1
Running hardware-aware neural architecture search on embedded devices under 512MB of RAM
Jun 12, 2026Andrea Mattia Garavagno, Edoardo Ragusa, Paolo Gastaldo +1Hardware-Aware NAS Convolutional Neural Networks
Department of Electrical, Electronic, Telecommunication Engineering and Naval Architecture, DITEN University of Genoa, Genoa 16145, Italy · Department of Excellence in Robotics & AI, Institute of Mechanical Intelligence, Scuola Superiore Sant’Anna, Pisa 56127, Italy
LLM-Guided Neural Architecture Search for Robust Co-Design of Physical Neural Networks
Jun 9, 2026Tyler King, Timothee LeleuEnergy-Efficient ML Large Language Model-Guided Optimization
NTT Research · Princeton University · Stanford University
Q-PhotoNAS: Hybrid Quantum Neural Architecture Search Framework on Photonic Devices
May 21, 2026Farah Elnakhal, Alberto Marchisio, Nouhaila Innan +2Hybrid Quantum-Classical ML Hardware-Aware NAS
Science Division, New York University Abu Dhabi, UAE · eBrain Lab, Division of Engineering, New York University Abu Dhabi, PO Box 129188, Abu Dhabi, UAE · Center for Quantum and Topological Systems, NYUAD Research Institute, New York University Abu Dhabi, UAE +1
AutoMCU: Feasibility-First MCU Neural Network Customization via LLM-based Multi-Agent Systems
May 20, 2026Penglin Dai, Zijie Zhou, Xincao Xu +3Efficient Neural Network Inference Hardware-Aware NAS
School of Computing and Artificial Intelligence, Southwest Jiaotong University, Chengdu 611756, China · Shenzhen Institute for Advanced Study, University of Electronic Science and Technology of China, Shenzhen 518110, China · School of Computer Science and Engineering, Northeastern University, Shenyang 110819, China
Hybrid Quantum-Classical Neural Architecture Search
May 18, 2026Alberto Marchisio, Muhammad Kashif, Nouhaila Innan +1Quantum Machine Learning Hybrid Quantum-Classical ML
eBRAIN Lab, Division of Engineering, New York University Abu Dhabi (NYUAD), Abu Dhabi, United Arab Emirates · Center for Quantum and Topological Systems (CQTS), NYUAD Research Institute, NYUAD, Abu Dhabi, United Arab Emirates

Oct 23, 2026J/K move · Enter open · S save

MOSHO: A Native Discrete Shark-Inspired Metaheuristic for Hardware-Aware Neural Architecture Search

Nazim Abdeerhmane Aouni, Zakaria Soualah Mohammed, Maroua Ogab, Nour El Imane Elbar+6

Higher National School of Computer Science (ESI), Algiers, Algeria · IBM Research

Hardware-Aware NAS Metaheuristic Optimization Multi-Objective Optimization Combinatorial Optimization

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Hardware-aware neural architecture search (NAS) requires jointly optimizing predictive accuracy and device-specific latency over discrete search spaces. While multi-objective swarm optimizers show promise, continuous formulations such as the Multi-Objective White Shark Optimizer (MOWSO) can suffer from quantization noise when rounded to integer-encoded architecture vectors. To address this limitation, we propose MOSHO (Multi-Objective Shark Hunting Optimization), a native discrete metaheuristic that replaces continuous motion equations with discrete architecture-editing operators. MOSHO integrates adaptive operator scheduling, energy-regulated restarts, and deferred crowding-distance truncation to enhance exploration and search efficiency. On the merged NAS-Bench-201/HW-NAS-Bench benchmark across 18 contexts, MOSHO achieves a median normalized hypervolume of 0.9987 and IGD+ of 0.0064, yielding Pareto fronts competitive with NSGA-II while reducing search runtime by 96.4% (0.64 s vs. 17.72 s). These results demonstrate that tailored discrete search mechanisms can enable swarm optimizers to achieve high-quality multi-objective solution quality with substantially lower search cost in hardware-aware NAS.