Hardware-Aware NAS

NAS: Neural Architecture Search

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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
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
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 +7Hardware-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
LLMForge: Multi-Backend Hardware-Aware Neural Architecture Search with Infinite-Head Attention for Edge Language Models
May 17, 2026Xinting Jiang, Junyi Luo, Ruichen Qi +4On-Device Language Model Inference Hardware-Aware NAS
Brown University · University of Michigan · Google Research
Surrogate Neural Architecture Codesign Package (SNAC-Pack)
May 15, 2026Jason Weitz, Dmitri Demler, Benjamin Hawks +3Hardware-Aware NAS FPGA-Based Neural Network Acceleration
University of California San Diego · 2ETH Zurich · 3Fermi National Accelerator Laboratory +1
XiYOLO: Energy-Aware Object Detection via Iterative Architecture Search and Scaling
May 7, 2026Tony Tran, Richie R. Suganda, Bin HuEnergy-Efficient ML Edge Inference
Department of Research Computing University of Houston Houston, TX 77479 · Department of Electrical and Computer Engineering University of Houston Houston, TX 77479 · Department of Engineering Technology University of Houston Houston, TX 77479
Resource-Aware Evolutionary Neural Architecture Search for Cardiac MRI Segmentation
May 7, 2026Farhana Yasmin, Mahade Hasan, Haipeng Liu +3Medical Image Analysis Medical Image Segmentation
School of Computer Science, Nanjing University of Information Science and Technology, Nanjing, China. · 3Eastern University, Ashulia Model Town, Dhaka, 1345, Bangladesh. · School of Software, Nanjing University of Information Science and Technology, Nanjing, China.
Hardware-Aware Neural Feature Extraction for Resource-Constrained Devices
May 5, 2026Francesco Tosini, Simone Pedroni, Christian Veronesi +5Edge Inference Visual SLAM
Department of Electronics, Information and Bioengineering (DEIB), Politecnico di Milano, Italy · Department of Electronics, Informationة · Smart Eyewear Lab, EssilorLuxottica, Milan, Italy
HERCULES: Hardware-Efficient, Robust, Continual Learning Neural Architecture Search
May 3, 2026Matteo Gambella, Fabrizio Pittorino, Manuel RoveriHardware-Aware NAS Neural Architecture Search
Dipartimento di Elettronica, Informazione e Bioingegneria
Deployment-Aligned Low-Precision Neural Architecture Search for Spaceborne Edge AI
Apr 27, 2026Parampuneet Kaur Thind, Vaibhav Katturu, Giacomo Zema +1Edge Computing Hardware-Aware NAS
1Sapienza University of Rome · 2 Φ-lab, ESA ESRIN · 4Argotec Srl +1
Algorithm and Hardware Co-Design for Efficient Complex-Valued Uncertainty Estimation
Apr 21, 2026Zehuan Zhang, Mark Chen, He Li +1Bayesian Neural Networks Uncertainty Quantification
Department of Computing, Imperial College London · 2Southeast University

Sep 2, 2026, cs.AIJ/K move · Enter open · S save

Measurement-Driven Sub-Network Selection for On-Premise Retrieval-Augmented Factory Agents

Vasileios Rizeakos, Georgios Paisios, Alexandros Machairas, Michael Birbas+1

AI lab of enakronIC PC · Aspasias 72, 15561, Cholargos, Greece · Electrical & Computer Engineering+2

Hardware-Aware NAS Edge Computing Retrieval-Augmented Generation Neural Network Pruning

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On-premise assistants can give factory workers conversational access to machine documentation, but models capable of the task rarely fit shop-floor hardware. We show that after structural compression and retrieval-grounded adaptation, model size is no longer a reliable predictor of adapted answer quality: general capability falls almost linearly with parameter count, while judged retrieval-augmented answer quality does not. We therefore treat deployment as a post-adaptation selection problem, committing one sub-network per device on judged answer quality and measured on-device throughput under a configurable general-capability floor and memory budget; rules that optimize size, speed, or quality alone each give up capability or throughput. A weight-shared supernetwork trained with sandwich-style in-place distillation keeps this selection inexpensive. In a manufacturing-manual case study, extraction costs 13.7 percent of the unpruned model's judged quality and retrieval-grounded distillation returns it to within 4.6 percent, recovering two thirds of the loss, and the same assistant runs across three heterogeneous edge tiers at 1.3 to 5 watts standby.

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