μNAS: Constrained Neural Architecture Search for Microcontrollers

Deep Residual Learning for Image Recognition

He, K., Zhang, X., Ren, S., & Sun, J. (2016) · 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2016.90 · Cited by 235,474 · see citing works

Going deeper with convolutions

Szegedy, C., Liu, W., Jia, Y., Sermanet, P., Reed, S., Anguelov, D., Erhan, D., Vanhoucke, V., & Rabinovich, A. (2015) · 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2015.7298594 · Cited by 48,562 · see citing works

Learning Multiple Layers of Features from Tiny Images

Krizhevsky, A. (2024) · doi:10.57702/zp44cu3g · Cited by 23,890 · see citing works

Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference

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MnasNet: Platform-Aware Neural Architecture Search for Mobile

Tan, M., Chen, B., Pang, R., Vasudevan, V., Sandler, M., Howard, A., & Le, Q. V. (2019) · 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR) · doi:10.1109/cvpr.2019.00293 · Cited by 3,416 · see citing works

Auto-Keras: An Efficient Neural Architecture Search System

Jin, H., Song, Q., & Hu, X. (2019) · ACM SIGKDD Conference on Knowledge Discovery and Data Mining (KDD) · doi:10.1145/3292500.3330648 · Cited by 963 · see citing works

CHARACTER RECOGNITION IN NATURAL IMAGES

de Campos, T. E., Babu, B. R., & Varma, M. (2009) · Cited by 449 · see citing works

Regularized Evolution for Image Classifier Architecture Search

Real, E., Aggarwal, A., Huang, Y., & Le, Q. V. (2019) · Proceedings of the AAAI Conference on Artificial Intelligence · doi:10.1609/aaai.v33i01.33014780 · Cited by 328 · see citing works

Neural Architecture Search with Bayesian Optimisation and Optimal Transport

Kandasamy, K., Neiswanger, W., Schneider, J., Póczos, B., & Xing, E. P. (2018) · neural information processing systems · Cited by 172 · see citing works

SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained MicrocontrollersPreprint

Fedorov, I., Adams, R. P., Mattina, M., & Whatmough, P. N. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.12107 · Cited by 130 · see citing works

A Flexible Framework for Multi-Objective Bayesian Optimization using Random ScalarizationsPreprint

Paria, B., Kandasamy, K., & Póczos, B. (2018) · arXiv (Cornell University) · doi:10.48550/arxiv.1805.12168 · Cited by 31 · see citing works

CoopNet: Cooperative Convolutional Neural Network for Low-Power MCUs

Mocerino, L., & Calimera, A. (2019) · IEEE International Conference on Electronics, Circuits and Systems (ICECS) · doi:10.1109/icecs46596.2019.8964993 · Cited by 9 · see citing works

A review on TinyML: State-of-the-art and prospects

Ray, P. P. (2022) · Journal of King Saud University - Computer and Information Sciences · doi:10.1016/j.jksuci.2021.11.019 · Cited by 460 · see citing works

Machine Learning for Microcontroller-Class Hardware: A Review

Saha, S. S., Sandha, S. S., & Srivastava, M. B. (2022) · IEEE Sensors Journal · doi:10.1109/jsen.2022.3210773 · Cited by 275 · see citing works

A Machine Learning-Oriented Survey on Tiny Machine Learning

Capogrosso, L., Cunico, F., Cheng, D. S., Fummi, F., & Cristani, M. (2024) · IEEE Access · doi:10.1109/access.2024.3365349 · Cited by 182 · see citing works

WiFi Sensing on the Edge: Signal Processing Techniques and Challenges for Real-World Systems

Hernandez, S. M., & Bulut, E. (2022) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2022.3209144 · Cited by 147 · see citing works

Neural Architecture Search Survey: A Hardware Perspective

Chitty-Venkata, K. T., & Somani, A. K. (2023) · ACM Computing Surveys · doi:10.1145/3524500 · Cited by 106 · see citing works

Systematic review on neural architecture search

Avval, S. S. P., Eskue, N., Groves, R. M., & Yaghoubi, V. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-024-11058-w · Cited by 100 · see citing works

A Comprehensive Survey on Hardware-Aware Neural Architecture SearchPreprint

Benmeziane, H., Maghraoui, K. E., Ouarnoughi, H., Niar, S., Wistuba, M., & Wang, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2101.09336 · Cited by 78 · see citing works

Enabling Resource-Efficient AIoT System With Cross-Level Optimization: A Survey

Liu, S., Guo, B., Fang, C., Wang, Z., Luo, S., Zhou, Z., & Yu, Z. (2023) · IEEE Communications Surveys & Tutorials · doi:10.1109/comst.2023.3319952 · Cited by 73 · see citing works

MCUNetV2: Memory-Efficient Patch-based Inference for Tiny Deep LearningPreprint

Lin, J., Chen, W.-M., Cai, H., Gan, C., & Han, S. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2110.15352 · Cited by 67 · see citing works

Neural Architecture Search and Hardware Accelerator Co-Search: A Survey

Sekanina, L. (2021) · IEEE Access · doi:10.1109/access.2021.3126685 · Cited by 50 · see citing works

Flexible and Fully Quantized Lightweight TinyissimoYOLO for Ultra-Low-Power Edge Systems

Moosmann, J., Müller, H., Zimmerman, N., Rutishauser, G., Benini, L., & Magno, M. (2024) · IEEE Access · doi:10.1109/access.2024.3404878 · Cited by 35 · see citing works

Enhancing Neural Architecture Search With Multiple Hardware Constraints for Deep Learning Model Deployment on Tiny IoT Devices

Burrello, A., Risso, M., Motetti, B. A., Macii, E., Luca Benini, C., & Pagliari, D. J. (2023) · IEEE Transactions on Emerging Topics in Computing · doi:10.1109/tetc.2023.3322033 · Cited by 30 · see citing works

Joint Pruning and Channel-Wise Mixed-Precision Quantization for Efficient Deep Neural Networks

Motetti, B. A., Risso, M., Burrello, A., Macii, E., Poncino, M., & Pagliari, D. J. (2024) · IEEE Transactions on Computers · doi:10.1109/tc.2024.3449084 · Cited by 30 · see citing works

Differentiable Neural Network Pruning to Enable Smart Applications on Microcontrollers

Liberis, E., & Lane, N. D. (2022) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3569468 · Cited by 29 · see citing works

HarvNet: Resource-Optimized Operation of Multi-Exit Deep Neural Networks on Energy Harvesting Devices

Jeon, S., Choi, Y., Cho, Y., & Cha, H. (2023) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3581791.3596845 · Cited by 25 · see citing works

TinyNS: Platform-aware Neurosymbolic Auto Tiny Machine Learning

Saha, S. S., Sandha, S. S., Aggarwal, M., Wang, B., Han, L., Briseno, J. D. G., & Srivastava, M. B. (2023) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3603171 · Cited by 24 · see citing works

Deep learning on microcontrollers

Svoboda, F., Fernández-Marqués, J., Liberis, E., & Lane, N. D. (2022) · Workshop on Machine Learning and Systems (EuroMLSys) · doi:10.1145/3517207.3526978 · Cited by 22 · see citing works

Optimizing Convolutional Neural Networks for Image Classification on Resource-Constrained Microcontroller Units

Brockmann, S., & Schlippe, T. (2024) · Computers · doi:10.3390/computers13070173 · Cited by 19 · see citing works

Enhancing the Energy Efficiency and Robustness of tinyML Computer Vision Using Coarsely-quantized Log-gradient Input Images

Lu, Q., & Murmann, B. (2023) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3591466 · Cited by 19 · see citing works

An Affordable Hardware-Aware Neural Architecture Search for Deploying Convolutional Neural Networks on Ultra-Low-Power Computing Platforms

Garavagno, A. M., Ragusa, E., Frisoli, A., & Gastaldo, P. (2024) · IEEE Sensors Letters · doi:10.1109/lsens.2024.3387056 · Cited by 17 · see citing works

Auritus

Saha, S. S., Sandha, S. S., Pei, S., Jain, V., Wang, Z., Li, Y., Sarker, A., & Srivastava, M. B. (2022) · Proceedings of the ACM on Interactive Mobile Wearable and Ubiquitous Technologies · doi:10.1145/3534586 · Cited by 17 · see citing works

Adaptive Subsampling for ROI-Based Visual Tracking: Algorithms and FPGA Implementation

Iqbal, O., Muro, V. I. T., Katoch, S., Spanias, A., & Jayasuriya, S. (2022) · IEEE Access · doi:10.1109/access.2022.3200755 · Cited by 17 · see citing works

Energy-Aware Integrated Neural Architecture Search and Partitioning for Distributed Internet of Things (IoT)

Huang, B., Abtahi, A., & Aminifar, A. (2024) · IEEE transactions on circuits and systems for artificial intelligence. · doi:10.1109/tcasai.2024.3493036 · Cited by 15 · see citing works

AnalogNAS: A Neural Network Design Framework for Accurate Inference with Analog In-Memory Computing

Benmeziane, H., Lammie, C., Boybat, I., Rasch, M. J., Gallo, M. L., Tsai, H.-Y. S., Muralidhar, R., Niar, S., Ouarnoughi, H., Narayanan, V., et al. (2023) · IEEE International Conference on Edge Computing and Communications (EDGE) · doi:10.1109/edge60047.2023.00045 · Cited by 15 · see citing works

LocoMote: AI-Driven Sensor Tags for Fine-Grained Undersea Localization and Sensing

Saha, S. S., Davis, C. W., Sandha, S. S., Park, J., Geronimo, J., Garcia, L. A., & Srivastava, M. B. (2024) · IEEE Sensors Journal · doi:10.1109/jsen.2024.3383721 · Cited by 14 · see citing works

AutoML for On-Sensor Tiny Machine Learning

Chowdhary, M., Lilienthal, D., Saha, S. S., & Palle, K. C. (2023) · IEEE Sensors Letters · doi:10.1109/lsens.2023.3327914 · Cited by 14 · see citing works

emoDARTS: Joint Optimization of CNN and Sequential Neural Network Architectures for Superior Speech Emotion Recognition

Rajapakshe, T., Rana, R. K., Khalifa, S., Şişman, B., Schuller, B. W., & Busso, C. (2024) · IEEE Access · doi:10.1109/access.2024.3439604 · Cited by 14 · see citing works

$μ$NAS: Constrained Neural Architecture Search for MicrocontrollersPreprint

Liberis, E., Dudziak, Ł., & Lane, N. D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.14246 · Cited by 14 · see citing works

MicroNAS for memory and latency constrained hardware aware neural architecture search in time series classification on microcontrollers

King, T., Zhou, Y., Röddiger, T., & Beigl, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-90764-z · Cited by 13 · see citing works

CNAS: Constrained Neural Architecture Search

Gambella, M., Falcetta, A., & Roveri, M. (2022) · Conference proceedings/Conference proceedings - IEEE International Conference on Systems, Man, and Cybernetics · doi:10.1109/smc53654.2022.9945080 · Cited by 11 · see citing works

A Quantitative Review of Automated Neural Search and On-Device Learning for Tiny Devices

Pau, D. P., Ambrose, P. K., & Aymone, F. M. (2023) · Chips · doi:10.3390/chips2020008 · Cited by 11 · see citing works

Synergy: Towards On-Body AI via Tiny AI Accelerator Collaboration on Wearables

Gong, T., Jang, S. Y., Acer, U. G., Kawsar, F., & Min, C. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3564314 · Cited by 11 · see citing works

Enabling ImageNet-Scale Deep Learning on MCUs for Accurate and Efficient Inference

Sadiq, S., Hare, J. S., Craske, S., Maji, P., & Merrett, G. V. (2023) · IEEE Internet of Things Journal · doi:10.1109/jiot.2023.3331654 · Cited by 10 · see citing works

Black-box models for non-functional properties of AI software systems

Friesel, B. K., & Spinczyk, O. (2022) · IEEE/ACM International Conference on AI Engineering – Software Engineering for AI (CAIN) · doi:10.1145/3522664.3528602 · Cited by 8 · see citing works

Inertial Navigation on Extremely Resource-Constrained Platforms: Methods, Opportunities and Challenges

Saha, S. S., Du, Y., Sandha, S. S., Garcia, L. A., Jawed, M. K., & Srivastava, M. B. (2023) · IEEE/ION Position Location and Navigation Symposium · doi:10.1109/plans53410.2023.10139997 · Cited by 8 · see citing works

Integration between constrained optimization and deep networks: a survey

Bizzarri, A., Fraccaroli, M., Lamma, E., & Riguzzi, F. (2024) · Frontiers in Artificial Intelligence · doi:10.3389/frai.2024.1414707 · Cited by 7 · see citing works

Exploring the power of lightweight YOLOv4

Wang, C.-Y., Liao, H.-Y. M., Yeh, I.-H., Chuang, Y., & Lin, Y.-L. (2021) · IEEE International Conference on Computer Vision Workshops · doi:10.1109/iccvw54120.2021.00092 · Cited by 7 · see citing works

Evolutionary Algorithms in Approximate Computing: A Survey

Sekanina, L. (2021) · Journal of Integrated Circuits and Systems · doi:10.29292/jics.v16i2.499 · Cited by 7 · see citing works

TrafficNNode: Low Power Vehicle Sensing Platform for Smart Cities

Nguyen, J., Grimsley, R., & Iannucci, B. (2021) · IEEE International Conference on Smart Internet of Things (SmartIoT) · doi:10.1109/smartiot52359.2021.00051 · Cited by 6 · see citing works

Joint Framework of Curriculum Learning and Knowledge Distillation for Noise-Robust and Small-Footprint Keyword Spotting

Lim, J.-B., & Baek, Y. (2023) · IEEE Access · doi:10.1109/access.2023.3314191 · Cited by 6 · see citing works

MicroNAS: Zero-Shot Neural Architecture Search for MCUs

Qiao, Y., Xu, H., Zhang, Y., & Huang, S. (2024) · Design, Automation, and Test in Europe · doi:10.23919/date58400.2024.10546710 · Cited by 6 · see citing works

Symbolic knowledge injection meets intelligent agents: QoS metrics and experiments

Agiollo, A., Rafanelli, A., Magnini, M., Ciatto, G., & Omicini, A. (2023) · Autonomous Agents and Multi-Agent Systems · doi:10.1007/s10458-023-09609-6 · Cited by 18 · see citing works

Fast data aware neural architecture search via supernet accelerated evaluation

Njor, E., Banbury, C., & Fafoutis, X. (2025) · Internet of Things · doi:10.1016/j.iot.2025.101688 · Cited by 5 · see citing works

TinyMLOps for real-time ultra-low power MCUs applied to frame-based event classification

Lê, M. T., & Arbel, J. (2023) · Workshop on Machine Learning and Systems (EuroMLSys) · doi:10.1145/3578356.3592586 · Cited by 5 · see citing works

A Practical Framework for Designing and Deploying Tiny Deep Neural Networks on Microcontrollers

Zhuang, B., & Pau, D. P. (2024) · International Conference on Consumer Electronics · doi:10.1109/icce59016.2024.10444435 · Cited by 5 · see citing works

Decoupled Access-Execute Enabled DVFS for TinyML Deployments on STM32 Microcontrollers

Alvanaki, E. L., Katsaragakis, M., Masouros, D., Xydis, S., & Soudris, D. (2024) · Design, Automation, and Test in Europe · doi:10.23919/date58400.2024.10546540 · Cited by 5 · see citing works

Enhancing Efficiency in Multidevice Federated Learning through Data Selection

Mo, F., Malekzadeh, M., Chatterjee, S., Kawsar, F., & Mathur, A. (2025) · ACM/IEEE Symposium on Edge Computing (SEC) · doi:10.1145/3769102.3770628 · Cited by 5 · see citing works

Communication Failure Resilient Distributed Neural Network for Edge Devices

Jeong, J., Park, J., & Yang, H. (2021) · Electronics · doi:10.3390/electronics10141614 · Cited by 4 · see citing works

Hybrid Convolution and Vision Transformer NAS Search Space for TinyML Image Classification

Djajapermana, M., Reiber, M., Mueller-Gritschneder, D., & Schlichtmann, U. (2026) · Communications in computer and information science · doi:10.1007/978-3-032-25308-8_12 · Cited by 3 · see citing works

Energy-Efficient Distributed Inference across the Edge–Cloud Continuum Using Adaptive Model Compression

Hu, P., Qing, Z., Ding, T., Ying, T., Xue, F., & Wang, L. (2026) · International Conference on Artificial Intelligence, Big Data and Algorithms (CAIBDA) · doi:10.1109/caibda70336.2026.11621676 · Cited by 3 · see citing works

Designing a Classifier for Active Fire Detection From Multispectral Satellite Imagery Using Neural Architecture Search

Cassimon, A., Reiter, P., Mercelis, S., & Mets, K. (2025) · IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing · doi:10.1109/jstars.2025.3556550 · Cited by 3 · see citing works

End-to-end Evolutionary Neural Architecture Search for Microcontroller Units

Groh, R., & Kist, A. M. (2023) · IEEE International Conference on Omni-layer Intelligent Systems (COINS) · doi:10.1109/coins57856.2023.10189194 · Cited by 3 · see citing works

Designing Extremely Memory-Efficient CNNs for On-Device Vision Tasks

Lee, J., Park, Y., & Lee, S. (2024) · Lecture notes in computer science · doi:10.1007/978-981-96-0966-6_25 · Cited by 3 · see citing works

Transfer Learning for Convolutional Neural Networks in Tiny Deep Learning Environments

Fragkou, E., Lygnos, V., & Katsaros, D. (2022) · Panhellenic Conference on Informatics · doi:10.1145/3575879.3575984 · Cited by 3 · see citing works

Demo: On-device Puff Detection System for Smoking Cessation

Mukhopadhyay, S., Dey, S., & Ghose, A. (2023) · International Conference on Mobile Systems, Applications, and Services · doi:10.1145/3581791.3597284 · Cited by 3 · see citing works

Algorithm-System Co-design for Efficient and Hardware-Aware Embedded Machine Learning

Lin, J., Chen, W.-M., & Han, S. (2023) · Springer eBooks · doi:10.1007/978-3-031-39932-9_14 · Cited by 3 · see citing works

Hardware-Aware Evolutionary Approaches to Deep Neural Networks

Sekanina, L., Mrázek, V., & Piňos, M. (2023) · Genetic and evolutionary computation · doi:10.1007/978-981-99-3814-8_12 · Cited by 3 · see citing works

3D-O-RAN: Dynamic Data Driven Open Radio Access Network Systems

Restuccia, F., Blasch, E., Ashdown, A., Ashdown, J., & Turck, K. (2022) · IEEE Military Communications Conference (MILCOM) · doi:10.1109/milcom55135.2022.10017706 · Cited by 3 · see citing works

SolarML: Optimizing Sensing and Inference for Solar-Powered TinyML Platforms

Liu, H., Wang, Q., & Zúñiga, M. A. (2025) · Design, Automation, and Test in Europe · doi:10.23919/date64628.2025.10993050 · Cited by 2 · see citing works

Rising From Pieces: Effective Inference at the Edge via Robust Split ML

Weng, Y., Zheng, T., Chen, Z., Hu, M., & Luo, J. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3605382 · Cited by 2 · see citing works

MONAS: Efficient Zero-Shot Neural Architecture Search for MCUs

Qiao, Y., Xu, H., Zhang, Y., & Huang, S. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11227909 · Cited by 2 · see citing works

Efficient Resource-Aware Neural Architecture Search with a Neuro-Symbolic Approach

Bellodi, E., Bertozzi, D., Bizzarri, A., Favalli, M., Fraccaroli, M., & Zese, R. (2023) · IEEE International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) · doi:10.1109/mcsoc60832.2023.00034 · Cited by 2 · see citing works

Designing a Bare Minimum Face Recognition Architecture for Bare Metal Edge Devices: An Experience Report

Dey, S. (2023) · International Conference on AI-ML Systems (AIMLSystems) · doi:10.1145/3639856.3639875 · Cited by 2 · see citing works

ZIP-CNN: Design Space Exploration for CNN Implementation within a MCU

Garbay, T., Hachicha, K., Dobiáš, P., Pinna, A., Hocine, K., Dron, W., Lusich, P., Khalis, I., & Granado, B. (2024) · ACM Transactions on Embedded Computing Systems · doi:10.1145/3691343 · Cited by 2 · see citing works

Multi-objective Framework for Training and Hardware Co-optimization in FPGAs

Mansoori, M. A., & Casu, M. R. (2023) · Lecture notes in electrical engineering · doi:10.1007/978-3-031-30333-3_36 · Cited by 2 · see citing works

A cascade framework for on-device uncertainty-aware event detection on microcontrollers

Jia, H., Kwon, Y. D., Ma, D., Pham, N., Qendro, L., Vu, T., & Mascolo, C. (2026) · Pervasive and Mobile Computing · doi:10.1016/j.pmcj.2026.102208 · Cited by 1 · see citing works

Explainable AI-Guided Neural Architecture Search for Adversarial Robustness in Approximate DNNs

Siddique, A., & Hoque, K. A. (2025) · IEEE Transactions on Sustainable Computing · doi:10.1109/tsusc.2025.3561603 · Cited by 1 · see citing works

A Case Study of Artificial Neural Network Compression Methods for Resource-Constrained Multi-Label Classification

Hołda, P., Wasielewska-Michniewska, K., Ganzha, M., & Paprzycki, M. (2026) · Informatica · doi:10.15388/26-infor620 · Cited by 1 · see citing works

Exploring Neural Architecture Search (NAS) for Binary Neural Networks (BNNs) in the New WakeVision

Pighetti, A., Bellotti, F., Mohammady, S., Lazzaroni, L., Ballout, H., & Berta, R. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-3-032-17174-0_12 · Cited by 1 · see citing works

Entropy Sampling-Based Neural Architecture Search for Resource-Constrained Microcontroller Targets

Heidorn, C. J. A., Hannig, F., Riedelbauch, D., Strohmeyer, C., & Teich, J. (2026) · Design, Automation, and Test in Europe · doi:10.23919/date69613.2026.11539313 · Cited by 1 · see citing works

Systematic Pruning and Acceleration for TinyML on Extremely Weak IoT Devices

Cao, J., Han, L., Wang, K., Xiao, Z., & Li, Z. (2026) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2026.3712264 · Cited by 1 · see citing works

Physics Structure-Informed Neural Networks in the TinyML Pipeline: Antisymmetry Preservation, Deployment Accounting, and Training-Budget Sensitivity

Jurj, S. L. (2026) · Machine Learning and Knowledge Extraction · doi:10.3390/make8090267 · Cited by 1 · see citing works

NAS for Automated ML Deployment on Extreme Edge Devices

Mahendran, G., Thirumaran, G., Ali, S. I., & Jadon, V. R. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-981-97-9578-9_26 · Cited by 1 · see citing works

TinyML Techniques for running Machine Learning models on Edge Devices

Mukherjee, A., Ukil, A., Dey, S., & Kulkarni, G. (2022) · International Conference on AI-ML Systems (AIMLSystems) · doi:10.1145/3564121.3564812 · Cited by 1 · see citing works

Automated Generation of Accurate, Compact and Focused Crop and Weed Segmentation Models

Dasgupta, S., & Dey, S. (2025) · Lecture notes in computer science · doi:10.1007/978-3-031-91835-3_11 · Cited by 1 · see citing works

Comparing Neural Architectures to Find the Best Model Suited for Edge Devices

Singh, B., & Mansukhani, J. (2023) · Proceedings in adaptation, learning and optimization · doi:10.1007/978-3-031-31164-2_16

One-Shot Surrogate for Evolutionary Multiobjective Neural Architecture Search

Lyu, K., Gong, M., Li, H., Gao, Y., Wu, Y., Feng, D., Shi, J., & Lei, Y. (2024) · IEEE Congress on Evolutionary Computation (CEC) · doi:10.1109/cec60901.2024.10611773

Supporting in-Sensor Computing with Hardware-Aware Neural Architecture Search

Garavagno, A. M., Ragusa, E., Zunino, R., Frisoli, A., & Gastaldo, P. (2025) · Lecture notes in electrical engineering · doi:10.1007/978-3-031-71518-1_48

Neural Architecture Search for Ultra-low Memory Blood Glucose Forecasting on the Edge

Zein, H. A., van de Waterlaat, N., & Alkanat, T. (2025) · IEEE International Conference on Acoustics Speech and Signal Processing · doi:10.1109/icassp49660.2025.10890864

Automatic Generation of Highly Accurate Parameter-optimal Segmentation Models in Precision Agriculture

Dey, S., & Dasgupta, S. (2024) · Joint International Conference on Data Science & Management of Data (CODS-COMAD) · doi:10.1145/3703323.3704272

Steerable Zero-Shot Neural Architecture Search for Efficient Edge Inference

Narduzzi, S., Vuagniaux, R., Sharma, K., Liu, S., & Dunbar, L. A. (2025) · IEEE International Symposium on Circuits and Systems proceedings · doi:10.1109/iscas56072.2025.11044278

REDS: Resource-Efficient Deep Subnetworks for Dynamic Resource Constraints

Corti, F., Maag, B., Schauer, J., Pferschy, U., & Saukh, O. (2025) · IEEE Transactions on Mobile Computing · doi:10.1109/tmc.2025.3594214

TinyHAR-NAS: Tuning Lightweight Attention Networks for Human Activity Recognition on the Edge

Jana, U., Mukhopadhyay, S., Dey, S., Mukherjee, A., & Pal, A. (2025) · International Joint Conference on Neural Networks (IJCNN) · doi:10.1109/ijcnn64981.2025.11229387

Designing Extremely Memory-Efficient CNNs for On-device Vision and Audio Tasks

Park, Y., Lee, J., & Lee, S. (2026) · International Journal of Computer Vision · doi:10.1007/s11263-025-02688-w

Hardware Aware NAS for Optimized Embedded Texture Classification in Multisensory Glove

Youssef, H. A., Yaacoub, M., Raad, M., Valle, M., & Ibrahim, A. A. (2026) · Lecture notes in electrical engineering · doi:10.1007/978-3-032-17174-0_51

On-Device Intelligence for Energy-Autonomous Wireless Sensor Nodes: A Benchmarking Study on Energy and Complexity

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Zip-CNN

Garbay, T. (2023) · doi:10.70675/2b548ac9zfc75z4a72z90dazfc0e0d1f174e

Short Paper: Towards Real-Time ECG and EMG Modeling on μNPUs

Millar, J., Thangarajan, A. S., Chatterjee, S., & Haddadi, H. (2026) · doi:10.1145/3774906.3802787

xAutoGA: an automated deep learning software to educate users

Mohasel, S. M., & Aghaei, A. A. (2026) · Multimedia Tools and Applications · doi:10.1007/s11042-026-21942-y

Binarized Wake-Up of Conversational Agents on an Industry-Grade High-Performance Microcontroller

Mohammady, S., Ballout, H., Bellotti, F., Lazzaroni, L., Berta, R., Fresta, M., Saad, A., & Pighetti, A. (2026) · IEEE International Conference on Omni-layer Intelligent Systems (COINS) · doi:10.1109/coins69995.2026.11715584

A 280-Parameter Embedding-Constrained Network for Autonomous Rate-of-Penetration Optimization on a Microcontroller

Zhi, G., Fu, C., Zheng, B., Bai, Z., & Wang, Y. (2026) · Applied Sciences · doi:10.3390/app162110465

$\mu$NAS: Constrained Neural Architecture Search for Microcontrollers.Preprint

Liberis, E., Dudziak, Ł., & Lane, N. D. (2020) · arXiv (Cornell University) · Cited by 4 · see citing works

$μ$NAS: Constrained Neural Architecture Search for MicrocontrollersPreprint

Liberis, E., Dudziak, Ł., & Lane, N. D. (2020) · arXiv (Cornell University) · doi:10.48550/arxiv.2010.14246 · Cited by 14 · see citing works

MicroNAS: Zero-Shot Neural Architecture Search for MCUs

Qiao, Y., Xu, H., Zhang, Y., & Huang, S. (2024) · Design, Automation, and Test in Europe · doi:10.23919/date58400.2024.10546710 · Cited by 6 · see citing works

MicroNAS for memory and latency constrained hardware aware neural architecture search in time series classification on microcontrollers

King, T., Zhou, Y., Röddiger, T., & Beigl, M. (2025) · Scientific Reports · doi:10.1038/s41598-025-90764-z · Cited by 13 · see citing works

SpArSe: Sparse Architecture Search for CNNs on Resource-Constrained MicrocontrollersPreprint

Fedorov, I., Adams, R. P., Mattina, M., & Whatmough, P. N. (2019) · arXiv (Cornell University) · doi:10.48550/arxiv.1905.12107 · Cited by 130 · see citing works