An Overview of FPGA Based Deep Learning Accelerators: Challenges and Opportunities

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DoReFa-Net: Training Low Bitwidth Convolutional Neural Networks with Low Bitwidth Gradients

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Going Deeper with Embedded FPGA Platform for Convolutional Neural Network

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Computer vision algorithms and hardware implementations: A survey

Feng, X., Jiang, Y., Yang, X., Du, M., & Li, X. S. (2019) · Integration · doi:10.1016/j.vlsi.2019.07.005 · Cited by 381

A Survey on Edge Computing Systems and Tools

Liu, F., Tang, G., Li, Y., Cai, Z., Zhang, X., & Zhou, T. (2019) · Proceedings of the IEEE · doi:10.1109/jproc.2019.2920341 · Cited by 361

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 214

Efficient Hardware Architectures for Accelerating Deep Neural Networks: Survey

Pudi, D., Boppu, S., Manikandan, M. S., & Cenkeramaddi, L. R. (2022) · IEEE Access · doi:10.1109/access.2022.3229767 · Cited by 155

Semantics for Robotic Mapping, Perception and Interaction: A Survey

Garg, S., Sünderhauf, N., Dayoub, F., Morrison, D., Cosgun, A., Carneiro, G., Wu, Q., Chin, T.-J., Reid, I. D., Gould, S. J., et al. (2020) · Foundations and Trends in Robotics · doi:10.1561/2300000059 · Cited by 124

A Survey of Convolutional Neural Networks on Edge with Reconfigurable Computing

Véstias, M. P. (2019) · Algorithms · doi:10.3390/a12080154 · Cited by 119

Deep learning in electron microscopy

Ede, J. M. (2020) · Machine Learning Science and Technology · doi:10.1088/2632-2153/abd614 · Cited by 118

Embedded Intelligence on FPGA: Survey, Applications and Challenges

Seng, K. P., Lee, P. J., & Ang, L. M. (2021) · Electronics · doi:10.3390/electronics10080895 · Cited by 105

FPGA-based Deep Learning Inference Accelerators: Where Are We Standing?

Nechi, A., Groth, L., Mulhem, S., Merchant, F., Buchty, R., & Bereković, M. (2023) · ACM Transactions on Reconfigurable Technology and Systems · doi:10.1145/3613963 · Cited by 94

Graph Neural Networks for Particle Tracking and Reconstruction

Duarte, J. M., & Vlimant, J.-R. (2022) · WORLD SCIENTIFIC eBooks · doi:10.1142/9789811234026_0012 · Cited by 80

Charged Particle Tracking via Edge-Classifying Interaction Networks

Dezoort, G., Thais, S. J., Duarte, J. M., Razavimaleki, V., Atkinson, M. J., Ojalvo, I., Neubauer, M. S., & Elmer, P. (2021) · Computing and Software for Big Science · doi:10.1007/s41781-021-00073-z · Cited by 66

Matrix Engines for High Performance Computing: A Paragon of Performance or Grasping at Straws?

Domke, J., Vatai, E., Валентинович, Д. А., ChenT, P., Oyama, Y., Zhang, L., Salaria, S., Mukunoki, D., Podobas, A., WahibT, M., et al. (2021) · IEEE International Parallel and Distributed Processing Symposium (IPDPS) · doi:10.1109/ipdps49936.2021.00114 · Cited by 35

Advanced deep learning and large language models: Comprehensive insights for cancer detection

Habchi, Y., Kheddar, H., Himeur, Y., Belouchrani, A., Serpedin, E., Khelifi, F., & Chowdhury, M. E. H. (2025) · Image and Vision Computing · doi:10.1016/j.imavis.2025.105495 · Cited by 34

Embedded Artificial Intelligence: A Comprehensive Literature Review

Huang, X., Wang, H., Qin, S., & Tang, S.-K. (2025) · Electronics · doi:10.3390/electronics14173468 · Cited by 34

MBBNet: An edge IoT computing-based traffic light detection solution for autonomous bus

Ouyang, Z., Niu, J., Ren, T., Li, Y., Cui, J., & Wu, J. (2020) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2020.101835 · Cited by 32

FPG-AI: A Technology-Independent Framework for the Automation of CNN Deployment on FPGAs

Pacini, T., Rapuano, E., & Fanucci, L. (2023) · IEEE Access · doi:10.1109/access.2023.3263392 · Cited by 31

Review and application of Edge AI solutions for mobile collaborative robotic platforms

Ramasubramanian, A. K., Mathew, R., Preet, I., & Papakostas, N. (2022) · Procedia CIRP · doi:10.1016/j.procir.2022.05.112 · Cited by 27

Fast machine learning for building management systems

Mshragi, M., & Petri, I. (2025) · Artificial Intelligence Review · doi:10.1007/s10462-025-11226-6 · Cited by 25

An analysis of voltage source inverter switches fault classification using short time Fourier transform

Manap, M., Nikolovski, S., Skamyin, A. N., Karim, R., Sutikno, T., & Jopri, M. H. (2021) · International Journal of Power Electronics and Drive Systems/International Journal of Electrical and Computer Engineering · doi:10.11591/ijpeds.v12.i4.pp2209-2220 · Cited by 23

A Review of FPGA‐Based Custom Computing Architecture for Convolutional Neural Network Inference

Xiyuan, P., Yu, J., Yao, B., Liansheng, L., & Peng, Y. (2021) · Chinese Journal of Electronics · doi:10.1049/cje.2020.11.002 · Cited by 23

Embedded Deep Inference in Practice: Case for Model Partitioning

Dey, S., Mukherjee, A., Pal, A., & Balamuralidhar, P. (2019) · doi:10.1145/3362743.3362964 · Cited by 22

FCM Clustering Approach Optimization Using Parallel High-Speed Intel FPGA Technology

Almomany, A., Jarrah, A. A., & Assaf, A. A. (2022) · Journal of Electrical and Computer Engineering · doi:10.1155/2022/8260283 · Cited by 22

A system-on-chip solution for deep learning-based automatic fetal biometric measurement

Cho, H., Kim, D., Chang, S., Kang, J., & Yoo, Y. (2023) · Expert Systems with Applications · doi:10.1016/j.eswa.2023.121482 · Cited by 21

FPGA-based neural network accelerators for millimeter-wave radio-over-fiber systems

Lee, J., He, J., & Wang, K. (2020) · Optics Express · doi:10.1364/oe.391050 · Cited by 20

Learning on Hardware: A Tutorial on Neural Network Accelerators and Co-ProcessorsPreprint

Baischer, L., Wess, M., & TaheriNejad, N. (2021) · arXiv (Cornell University) · doi:10.48550/arxiv.2104.09252 · Cited by 20

Area and power efficient pipelined hybrid merged adders for customized deep learning framework for FPGA implementation

Kowsalya, T. (2019) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2019.102906 · Cited by 18

An Energy Efficient EdgeAI Autoencoder Accelerator for Reinforcement Learning

Manjunath, N. K., Shiri, A., Hosseini, M., Prakash, B., Waytowich, N. R., & Mohsenin, T. (2021) · IEEE Open Journal of Circuits and Systems · doi:10.1109/ojcas.2020.3043737 · Cited by 17

AMP4EC: Adaptive Model Partitioning Framework for Efficient Deep Learning Inference in Edge Computing Environments

Zhang, G., Guo, W., Tan, Z., & Jiang, H. (2025) · International Conference on Fog and Mobile Edge Computing (FMEC) · doi:10.1109/fmec65595.2025.11119384 · Cited by 15

Exploring energy efficiency of LSTM accelerators: A parameterized architecture design for embedded FPGAs

Qian, C., Ling, T., & Schiele, G. (2024) · Journal of Systems Architecture · doi:10.1016/j.sysarc.2024.103181 · Cited by 14

Designing Deep Learning Hardware Accelerator and Efficiency Evaluation

Qi, Z., Chen, W., Naqvi, R. A., & Siddique, K. (2022) · Computational Intelligence and Neuroscience · doi:10.1155/2022/1291103 · Cited by 13

A Performance Study of Deep Neural Network Representations of Interpretable ML on Edge Devices with AI Accelerators

Schauer, J., Goodarzi, P., Morsch, J., & Schütze, A. (2025) · Sensors · doi:10.3390/s25185681 · Cited by 11

FPGA virtualization deployment based on Docker container technology

Long, X., Liu, B., Jiang, F. X., Zhang, Q., & Zhi, X. (2020) · International Conference on Mechanical, Control and Computer Engineering (ICMCCE) · doi:10.1109/icmcce51767.2020.00109 · Cited by 11

Nimblock: Scheduling for Fine-grained FPGA Sharing through Virtualization

Mandava, M., Reckamp, P., & Chen, D. (2023) · Proceedings - International Symposium on Computer Architecture/Proceedings · doi:10.1145/3579371.3589095 · Cited by 11

A survey of field programmable gate array (FPGA)-based graph convolutional neural network accelerators: challenges and opportunities

Li, S., Tao, Y., Tang, E., Xie, T., & Chen, R. (2022) · PeerJ Computer Science · doi:10.7717/peerj-cs.1166 · Cited by 11

Design and Performance Comparison of CNN Accelerators Based on the Intel Movidius Myriad2 SoC and FPGA Embedded Prototype

Kyriakos, A., Papatheofanous, E. A., Bezaitis, C., Petrongonas, E., Soudris, D., & Reisis, D. I. (2019) · International Conference on Control, Artificial Intelligence, Robotics & Optimization (ICCAIRO) · doi:10.1109/iccairo47923.2019.00030 · Cited by 11

Vision-based robotics using open FPGAs

Machado, F., Nieto, R., Fernández-Conde, J., Lobato, D., & Cañas, J. M. (2023) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2023.104974 · Cited by 10

Design of a DSL for Converting Rust Programming Language into RTL

Takano, K., Oda, T., & Kohata, M. (2020) · Lecture notes on data engineering and communications technologies · doi:10.1007/978-3-030-39746-3_36 · Cited by 10

Accelerating FCM Algorithm Using High-Speed FPGA Reconfigurable Computing Architecture

Almomany, A., Jarrah, A. A., & Assaf, A. A. (2023) · Journal of Electrical Engineering and Technology · doi:10.1007/s42835-023-01432-z · Cited by 10

Review of Energy-Efficient Embedded System Acceleration of Convolution Neural Networks for Organic Weeding Robots

Czymmek, V., Köhn, C., Harders, L. O., & Hussmann, S. H. (2023) · Agriculture · doi:10.3390/agriculture13112103 · Cited by 10

Deep Learning in Reconfigurable Hardware: A Survey

Dias, M. A., & Ferreira, D. A. P. (2019) · IEEE International Symposium on Parallel & Distributed Processing, Workshops and Phd Forum · doi:10.1109/ipdpsw.2019.00026 · Cited by 10

Reconfigurable Edge Hardware for Intelligent IDS: Systematic Approach

Foudhaili, W., Nechi, A., Thermann, C., Johmani, M. A., Buchty, R., Bereković, M., & Mulhem, S. (2024) · Lecture notes in computer science · doi:10.1007/978-3-031-55673-9_4 · Cited by 9

Towards the Extension of FPG-AI Toolflow to RNN Deployment on FPGAs for On-board Satellite Applications

Pacini, T., Rapuano, E., Tuttobene, L., Nannipieri, P., Fanucci, L., & Moranti, S. (2023) · European Data Handling & Data Processing Conference (EDHPC) · doi:10.23919/edhpc59100.2023.10396607 · Cited by 8

A new microarchitecture hardware proposal for the use of the improved template matching method in face similarity detection

Dereli, S., & Ünsal, A. R. (2023) · Microprocessors and Microsystems · doi:10.1016/j.micpro.2023.104908 · Cited by 7

Joint UAV deployment, SF placement, and collaborative task scheduling in heterogeneous multi‐UAV‐empowered edge intelligence

Wang, Y., Wei, X., Wang, H., Fan, J., Chen, J., Zhao, K., & Hu, Y. (2023) · IET Communications · doi:10.1049/cmu2.12570 · Cited by 7

Characterizing and Optimizing Real-Time Optimal Control for Embedded SoCs

Dong, S. K., Nikiforov, D., Soedarmadji, W., Nguyen, M., Jain, V., Fletcher, C. W., & Shao, Y. S. (2025) · IEEE International Symposium on Workload Characterization · doi:10.1109/iiswc66894.2025.00047 · Cited by 6

An OpenCL-Based FPGA Accelerator for Compressed YOLOv2

Yang, A., Li, Y., Shu, H., Deng, J., Ma, C., Li, Z., & Wang, Q. (2019) · International Conference on Field-Programmable Technology (FPT) · doi:10.1109/icfpt47387.2019.00036 · Cited by 6

Simple Siamese Model with Long Short-Term Memory for User Authentication with Field-Programmable Gate Arrays

Choi, H. (2024) · Electronics · doi:10.3390/electronics13132584 · Cited by 5

Accelerating On-Device Learning with Layer-Wise Processor Selection Method on Unified Memory

Ha, D., Kim, M., Moon, K.-D., & Jeong, C. Y. (2021) · Sensors · doi:10.3390/s21072364 · Cited by 5

A novel utilization-aware and power-delay-aware intelligent DMA controller for video streaming used in AI applications

Santosh Kumar, B., & Krishna Kumar, E. (2022) · International Journal of Pervasive Computing and Communications · doi:10.1108/ijpcc-11-2021-0280 · Cited by 5

FPG-AI RNN: A Technology-Agnostic Framework for the Automatic Acceleration of LSTM/GRU-Based Models on FPGAs

Pacini, T., Nannipieri, P., Moranti, S., & Fanucci, L. (2025) · IEEE Access · doi:10.1109/access.2025.3577908 · Cited by 4

Deep learning-based electronics architecture for pulse pile-up correction in synchrotron radiation spectroscopic detection

Wang, S., Pan, N., Gu, S., Wang, Y., & Huang, Y. (2025) · Nuclear Instruments and Methods in Physics Research Section B Beam Interactions with Materials and Atoms · doi:10.1016/j.nimb.2025.165730 · Cited by 3

A FPGA-based accelerator implementaion for YOLOv2 object detection using Winograd algorithm

Lv, P. M., Liu, W., & Li, J. (2020) · International Conference on Mechanical, Control and Computer Engineering (ICMCCE) · doi:10.1109/icmcce51767.2020.00415 · Cited by 3

ICICOS: Industrial Cyber Intelligent Control Operating System for Cloud and Edge Computing

Su, W., Kong, Y., & He, Z. (2021) · IEEE International Conference on Smart Internet of Things (SmartIoT) · doi:10.1109/smartiot52359.2021.00056 · Cited by 3

Optimal hardware implementation for end-to-end CNN-based classification

Aydın, S. G., & Bılge, H. Ş. (2023) · International Conference on Innovative Trends in Information Technology (ICITIIT) · doi:10.1109/icitiit57246.2023.10068601 · Cited by 3

ASELGPU: FPGA-based OpenGL SC compatible DO-254 certifiable graphics processor engine

Eral, G., Kılınç, C., Akkul, E. S., & Kızılöz, C. (2022) · Signal Image and Video Processing · doi:10.1007/s11760-022-02370-5 · Cited by 2

Inference and Evaluation of Deep Convolutional Neural Networks on Microchip’s Hardware Accelerator VectorBlox

Dadà, M., Zulberti, L., Nannipieri, P., Fanucci, L., & Moranti, S. (2023) · European Data Handling & Data Processing Conference (EDHPC) · doi:10.23919/edhpc59100.2023.10396304 · Cited by 2

Hardware Implementation of a Deep Learning-based Autonomous System for Smart Homes using Field Programmable Gate Array Technology

Tounsi, M., Mahdi, A. J., Ahmed, M. A., Azar, A. T., Smait, D. A., Ahmed, S., Zalzala, A. M., & Ibraheem, I. K. (2024) · Engineering Technology & Applied Science Research · doi:10.48084/etasr.8372 · Cited by 2

Implementation and Analysis on FIFO using FPGA

Shrivastava, S., & Kaur, A. (2024) · International Conference on Sustainable Computing and Smart Systems (ICSCSS) · doi:10.1109/icscss60660.2024.10624886 · Cited by 2

Exponential Computing Digital Circuit Design Developed for FPGA-based Embedded Systems

Dereli, S., & Uç, M. (2020) · Academic Perspective Procedia · doi:10.33793/acperpro.03.01.59 · Cited by 2

Review of Side Channel Attacks and Countermeasures of FPGA Based Systems

Koh, J. Y., & Kumar, T. N. (2021) · IEEE Student Conference on Research and Development (SCOReD) · doi:10.1109/scored53546.2021.9652773 · Cited by 2

Breast cancer classification using deep learning and FPGA inferencing

Wong, E.-H., Zakaria, F. F., Mustapa, M., Warip, M. N. M., & Ehkan, P. (2023) · AIP conference proceedings · doi:10.1063/5.0111204 · Cited by 2

SMALE: Enhancing Scalability of Machine Learning Algorithms on Extreme-Scale Computing Platforms

Chen, Y., & Li, H. H. (2022) · OSTI OAI (U.S. Department of Energy Office of Scientific and Technical Information) · doi:10.2172/1846568 · Cited by 2

Hardware Implementation of Optimized LeNet-5

Ju, J., & Liu, C. (2022) · International Conference on Computational Modeling, Simulation and Data Analysis (CMSDA) · doi:10.1109/cmsda58069.2022.00010 · Cited by 1

An SoC-based CNN accelerator for face recognition using HWCK data scheduling

Tsai, T., & Hsu, C. (2025) · Multimedia Systems · doi:10.1007/s00530-025-01838-x · Cited by 1

Analysis of Research Progress on Deployment Methods for Deep Learning Models on FPGAs

Wang, S., Chen, L., Tian, C., Zhou, J., Zhang, Y., & Cao, Y. (2026) · Electronics · doi:10.3390/electronics15163536 · Cited by 1

USING ARTIFICIAL INTELLIGENCE ACCELERATORS TO TRAIN COMPUTER GAME CHARACTERS

Hnatchuk, Y., SIERHIEIEV, Y., & Hnatchuk, A. (2021) · Computer Systems and Information Technologies · doi:10.31891/csit-2021-3-9 · Cited by 1

Compression-Assisted Zero-Shot Prompting of Large Language Models (LLMs) for Educational Skill Classification of Microprocessor Curricula

Amoruso, P. E., & DeMara, R. F. (2025) · ACM Great Lakes Symposium on VLSI (GLSVLSI) · doi:10.1145/3716368.3735218 · Cited by 1

Generational Learning for Robust, Agile and Compliant Models in Dynamic Environments Through Temporal Knowledge Distillation

Shen, H., & Kurshan, E. (2026) · International Journal of Semantic Computing · doi:10.1142/s1793351x26500017 · Cited by 1

Temporal Knowledge Distillation for Time Sensitive Financial Services ApplicationsPreprint

Kurshan, E., & Shen, H. (2024) · SSRN Electronic Journal · doi:10.2139/ssrn.4941375 · Cited by 1

Künstliche Intelligenz für den Spitzensport im Spannungsfeld zwischen Big und Small Data (KISs-BiS)

Hagg, A., Asteroth, A., Rasche, C., Bach, K., Pfeiffer, M., & Strauß, S. (2021) · Cited by 1

Communication reduction techniques in numerical methods and deep neural networks

Zhuang, S. (2019) · UPCommons institutional repository (Universitat Politècnica de Catalunya) · doi:10.5821/dissertation-2117-173617

Efficient processing of vision kernels and deep neural networks on reconfigurable computing architectures

Qasaimeh, M. (2020) · Iowa State University Digital Repository (Iowa State University) · doi:10.31274/etd-20210114-116

The implementation of zero-shot algorithm on FPGA

Pan, Z., Wu, G., & Zou, H. (2020) · International Conference on Information Science and Control Engineering · doi:10.1109/icisce50968.2020.00491

FPGA Based Embedded Neural Network Object Detector

Baischer, L. (2021) · reposiTUm (TU Wien) · doi:10.34726/hss.2021.69314

Implementation of a number recognition algorithm built using a neural network on the BASYS3 FPGA panel

Mukhanbet, A., Nurakhov, E. S., & Daribayev, B. (2021) · Bulletin of the National Engineering Academy of the Republic of Kazakhstan · doi:10.47533/2020.1606-146x.119

Peer Review #2 of "A survey of field programmable gate array (FPGA)-based graph convolutional neural network accelerators: challenges and opportunities (v0.1)"

(2022) · PeerJ Computer Science · doi:10.7287/peerj-cs.1166v0.1/reviews/2

Peer Review #2 of "A survey of field programmable gate array (FPGA)-based graph convolutional neural network accelerators: challenges and opportunities (v0.2)"

(2022) · PeerJ Computer Science · doi:10.7287/peerj-cs.1166v0.2/reviews/2

Peer Review #1 of "A survey of field programmable gate array (FPGA)-based graph convolutional neural network accelerators: challenges and opportunities (v0.2)"

(2022) · PeerJ Computer Science · doi:10.7287/peerj-cs.1166v0.2/reviews/1

Peer Review #1 of "A survey of field programmable gate array (FPGA)-based graph convolutional neural network accelerators: challenges and opportunities (v0.1)"

(2022) · PeerJ Computer Science · doi:10.7287/peerj-cs.1166v0.1/reviews/1

High-speed operation, an FPGA-based convolutional neural network by pipelined accelerator

Umapathy, K., Sivanantham, E., Epsiba, P., Gopi, B., & Kumar, S. M. (2023) · AIP conference proceedings · doi:10.1063/5.0110693

Implications and Future Scope

Gupta, R., Das, S., & Pal, S. K. (2023) · Springer eBooks · doi:10.1007/978-981-19-9798-3_5

An efficient FPGA based convolutional neural networks by fully pipelined accelerator

Kasireddy, V., & Mishra, S. K. (2023) · AIP conference proceedings · doi:10.1063/5.0125188

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Jaballi, E., Gdaim, S., & Liouane, N. (2026) · International Journal of Advanced Computer Science and Applications · doi:10.14569/ijacsa.2026.0170775

FPGA-based brain tumor MRI classification using multi-scale discrete hahn moments and a lightweight CNN

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