Diego Leonel Cadette Dutra

@ufrj.br

Systems Engineering and Computer Science Program
Universidade Federal do Rio de Janeiro

Diego Leonel Cadette Dutra


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https://researchid.co/diegodutra
Diego Leonel Cadette Dutra is a Professor at the Federal University of Rio de Janeiro (UFRJ), Brazil, leading the HEADS research group and the COMPASS Laboratory. With a D. and M.Sc. (2009) in Systems Engineering and Computer Science from UFRJ, and postdoctoral experience at UFRJ and Aalto University. Professor Diego was also awarded the Young Scientist of our state scholarship by FAPERJ and CNPq productivity grant PQ-C.

EDUCATION

- D. and M.Sc. (2009) in Systems Engineering and Computer Science from UFRJ
- Postdoctoral experience at UFRJ and Aalto University

RESEARCH, TEACHING, or OTHER INTERESTS

Computer Engineering, Hardware and Architecture, Computer Networks and Communications
38

Scopus Publications

946

Scholar Citations

16

Scholar h-index

21

Scholar i10-index

Scopus Publications

  • Weightless Neural Networks on Flexible Substrates: A Novel Approach to Wearable Machine Learning
    Igor D. S. Miranda, Velu Pillai, Tejas Musale, Mugdha Jadhao, Paulo C. R. Souza Neto, Zachary Susskind, Alan Bacellar, Mael Lhostis, Priscila M. V. Lima, Diego L. C. Dutra, Eugene B. John, Mauricio Breternitz, Felipe M. G. França, Emre Ozer, Lizy K. John
    IEEE Transactions on Very Large Scale Integration VLSI Systems, 2026
    In this article, we present a novel approach that seamlessly integrates machine learning (ML) algorithms into wearable technology through the use of weightless neural networks (WNNs) and flexible integrated circuits (FlexICs). Our methodology employs combinational intelligent networks (COIN) for edge inference on resource-constrained devices, highlighting the advantages of WNNs in terms of power efficiency and minimal hardware requirements. We propose an automated design flow for implementing COIN as FlexICs aimed at developing scalable, cost-effective, and environmentally sustainable wearable monitoring solutions. As a proof-of-concept demonstrator, an arrhythmia detection FlexIC was fabricated using COIN to meet the stringent requirements of medium-complexity wearable applications, offering a promising path toward personalized and accessible healthcare solutions.
  • Analyzing Offshore Vessel Encounters: A Dataset for Enhancing Maritime Security and Monitoring
    Vinicius D. do Nascimento, Claudio M. de Farias, Diego L. C. Dutra, Tiago A. O. Alves
    Proceedings of the 2025 28th International Conference on Information Fusion Fusion 2025, 2025
    Maritime Situational Awareness (MSA) is crucial for identifying suspicious vessel activities, such as dark-ship operations and prolonged loitering activities. However, the development of robust detection systems requires high-quality datasets that capture vessel encounters, particularly encounters that occur beyond 20 nautical miles (NM) from the coast. This paper presents the creation and analysis of a comprehensive data set that contains vessel trajectories associated with offshore encounters. The dataset, constructed using 12 months of data from the Marine Cadastre Automatic Identification System (AIS), leverages the H3 geohash system for spatial proximity detection and MovingPandas for trajectory extraction. The dataset analysis demonstrates that the dataset is a powerful tool for enhancing Maritime Domain Awareness (MDA), contributing to monitoring and security in the maritime environment. The analysis of encounter patterns highlights both the importance of reliable data and the need for a robust detection system to address uncertainties and information gaps.
  • Edge-Optimized Weightless Neural Network for Low-Power Wearable Arrhythmia Detection
    Velu Pillai, Shashank Nag, Mugdha Jadhao, Alan Bacellar, Igor D. S. Miranda, Felipe M. G. França, Diego L. C. Dutra, Priscila M. V. Lima, Lizy K. John, Eugene B. John
    Proceedings of the IEEE Dallas Circuits and Systems Conference Dcas, 2025
    Cardiovascular disease (CVD) remains the leading cause of death worldwide. The standard diagnostic approach for CVD relies on electrocardiogram (ECG) analysis [1], which records the heart's electrical activity over time. However, the manual interpretation of the rapidly growing volume of ECG data demands considerable medical expertise and resources. This challenge has driven the development of automated ECG analysis techniques utilizing machine learning. In this paper, we present an innovative approach to integrating machine learning into a wearable medical patch. Leveraging the efficiency and low hardware complexity of Weightless Neural Networks (WNNs) combined with 45 nm CMOS Integrated Circuit technology, we introduce a specialized WNN model, “arrDWNN,” for arrhythmia detection from ECG signals. The arrDWNN architecture represents a significant advancement in wearable cardiac monitoring, enabling the development of next-generation low-cost ECG patches [2], for real-time arrhythmia detection. This edgecomputing solution integrates sensor functionality directly within the patch, facilitating real-time analysis of ECG signals for the immediate identification of potentially life-threatening arrhythmias. The proposed approach offers multiple advantages over traditional methods, including lower power consumption, continuous monitoring capabilities, and enhanced accessibility. By providing a rapid, cost-effective tool for detecting and managing cardiovascular events, the arrDWNN-based patch has the potential to revolutionize clinical practice, particularly in under served and low-income communities with limited access to specialized cardiac care worldwide. The proposed arrDWNN model achieves an impressive average accuracy of 89 % on the MIT-BIH [3] arrhythmia database. The hardware implementation [4], designed with 45 nm CMOS technology, features a compact circuit with 11,443 NAND2-equivalent gates and occupies an area of only 12.4 <tex xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">$\text{mm}^{2}$</tex> while operating at under 4.24 mW of power. This research highlights the potential of efficient, low-power machine learning solutions in wearable medical devices, enabling real-time cardiac monitoring to support preventive care and early intervention. In turn, this can significantly enhance patient outcomes while helping to alleviate the globalburden of cardiovascular disease.
  • A Hybrid Framework for Maritime Surveillance: Detecting Illegal Activities through Vessel Behaviors and Expert Rules Fusion
    Vinicius D. do Nascimento, Tiago A. O. Alves, Claudio M. de Farias, Diego Leonel Cadette Dutra
    Sensors, 2024
    Maritime traffic is essential for global trade but faces significant challenges, including navigation safety, environmental protection, and the prevention of illicit activities. This work presents a framework for detecting illegal activities carried out by vessels, combining navigation behavior detection models with rules based on expert knowledge. Using synthetic and real datasets based on the Automatic Identification System (AIS), we structured our framework into five levels based on the Joint Directors of Laboratories (JDL) model, efficiently integrating data from multiple sources. Activities are classified into four categories: illegal fishing, suspicious activity, anomalous activity, and normal activity. To address the issue of a lack of labels and integrate data-driven detection with expert knowledge, we employed a stack ensemble model along with active learning. The results showed that the framework was highly effective, achieving 99% accuracy in detecting illegal fishing and 92% in detecting suspicious activities. Furthermore, it drastically reduced the need for manual checks by specialists, transforming experts’ tacit knowledge into explicit knowledge through the models and allowing continuous updates of maritime domain rules. This work significantly contributes to maritime surveillance, offering a scalable and efficient solution for detecting illegal activities in the maritime domain.
  • Ensemble Learning Approaches for Detecting Fishing Activity in Maritime Surveillance: A Performance Evaluation
    Vinicius D. Do Nascimento, Claudio M. De Farias, Diego L. C. Dutra, Tiago A. O. Alves
    Fusion 2024 27th International Conference on Information Fusion, 2024
    Detecting fishing trajectories in maritime surveillance is of the utmost importance for identifying illegal fishing activity. In the event of illegal fishing activity, the maritime authority can mobilize resources to engage the vessel; hence, a false flag can be costly. This study investigates the efficacy of ensemble learning techniques for boosting individual model performance and decreasing uncertainty. Employing a range of machine learning models, including logistic regression, decision trees, random forests, neural networks, gradient boosting, and recurrent neural networks, the research evaluates the combination of these using ensemble methods like ensemble mean, weighted ensemble, and stacking approaches to enhance precision and decrease uncertainty. The primary dataset comprises a combination of fishing vessel and cargo vessel trajectories to train and test the models. Methodologically, the paper details the process of data analysis and the application of ensemble learning. A comparative assessment of individual models versus ensemble techniques forms the crux of this study. Results indicate a marked improvement in accuracy and consistency when employing ensemble methods, with weighted and stacking ensembles showing particular promise. These findings suggest that ensemble models outperform their individual counterparts in the context of maritime surveillance. This research makes a notable contribution to the maritime surveillance domain, demonstrating the potential of ensemble learning in enhancing detection capabilities for illegal fishing activities. The implications of these advancements are critical for maritime authorities as they strive to effectively monitor and protect marine ecosystems.
  • LogicNets vs. ULEEN : Comparing two novel high throughput edge ML inference techniques on FPGA
    Shashank Nag, Zachary Susskind, Aman Arora, Alan T. L. Bacellar, Diego L. C. Dutra, Igor D. S. Miranda, Krishnan Kailas, Eugene B. John, Mauricio Breternitz, Priscila M. V. Lima, Felipe M. G. França, Lizy K. John
    Midwest Symposium on Circuits and Systems, 2024
    With the advent of Internet-of-Things (IoT) and edge computing devices, there has been an increased demand for low power and high-throughput machine learning inference on the edge. However, the trends of ever-increasing model sizes with numerous computations involved makes it increasingly difficult to deploy state-of-the-art models on edge computing devices. Of late, there has been a renewed interest in lookup table (LUT)-based ML models that replace typical weighted-addition operations in artificial neurons with lookup operations. These are well suited for edge FPGAs, both due to their underlying architecture, as well as their potential for low energy consumption. LogicNets and ULEEN are two such LUT-based model architectures, that have claimed to offer high throughput and low energy inferences. These two architectures are extensions of contrasting ideas of Deep Neural Networks and Weightless Neural Networks, and it is difficult to infer a suitable choice among these. In this paper, we compare these, and evaluate them on some high-throughput inference use cases. When evaluated on intrusion detection and physics-experiment classification tasks, our results suggest that ULEEN outperforms LogicNets on hardware and energy requirements making it well suited for edge deployment, albeit at a slight drop in accuracy for some datasets.
  • arrWNN: Arrhythmia-detecting Weightless Neural Network FlexIC
    Velu Pillai, Igor D. S. Miranda, Tejas Musale, Mugdha Jadhao, Paulo C. R. Souza Neto, Zachary Susskind, Alan T. L. Bacellar, Mael Lhostis, Priscila M. V. Lima, Diego L. C. Dutra, Eugene B. John, Mauricio Breternitz, Felipe M. G. França, Emre Ozer, Lizy K. John
    6th IEEE International Flexible Electronics Technology Conference Ifetc 2024 Proceedings, 2024
    This paper proposes a technique for incorporating machine learning into a wearable medical patch by combining two key technologies: weightless neural networks (WNNs), known for their efficiency and low hardware overhead, and Flexible Integrated Circuits (FlexICs) - ultra low-cost circuits on flexible substrates. We develop a special WNN model called “arrWNN” for detecting arrhythmia events from ECG signals that has an average prediction accuracy of 89% over the MIT BIH Arrhythmia datasets. We, then, design and implement the arrWNN model in hardware, and fabricate it using Pragmatic's FlexIC technology. The arrWNN FlexIC contains 5,706 NAND2-equivalent gates with a core area of 24 mm<sup xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">2</sup> consuming less than 10 mW at 3V. Our wafer-level test and measurement results show the full functionality of the fabricated arrWNN FlexICs validated against the simulation.
  • ULEEN: A Novel Architecture for Ultra-low-energy Edge Neural Networks
    Zachary Susskind, Aman Arora, Igor D. S. Miranda, Alan T. L. Bacellar, Luis A. Q. Villon, Rafael F. Katopodis, Leandro S. de Araújo, Diego L. C. Dutra, Priscila M. V. Lima, Felipe M. G. França, Mauricio Breternitz Jr., Lizy K. John
    ACM Transactions on Architecture and Code Optimization, 2023
    ‘‘Extreme edge” 1 devices, such as smart sensors, are a uniquely challenging environment for the deployment of machine learning. The tiny energy budgets of these devices lie beyond what is feasible for conventional deep neural networks, particularly in high-throughput scenarios, requiring us to rethink how we approach edge inference. In this work, we propose ULEEN, a model and FPGA-based accelerator architecture based on weightless neural networks (WNNs). WNNs eliminate energy-intensive arithmetic operations, instead using table lookups to perform computation, which makes them theoretically well-suited for edge inference. However, WNNs have historically suffered from poor accuracy and excessive memory usage. ULEEN incorporates algorithmic improvements and a novel training strategy inspired by binary neural networks (BNNs) to make significant strides in addressing these issues. We compare ULEEN against BNNs in software and hardware using the four MLPerf Tiny datasets and MNIST. Our FPGA implementations of ULEEN accomplish classification at 4.0–14.3 million inferences per second, improving area-normalized throughput by an average of 3.6× and steady-state energy efficiency by an average of 7.1× compared to the FPGA-based Xilinx FINN BNN inference platform. While ULEEN is not a universally applicable machine learning model, we demonstrate that it can be an excellent choice for certain applications in energy- and latency-critical edge environments.
  • A conditional branch predictor based on weightless neural networks
    Luis A.Q. Villon, Zachary Susskind, Alan T.L. Bacellar, Igor D.S. Miranda, Leandro S. de Araújo, Priscila M.V. Lima, Mauricio Breternitz, Lizy K. John, Felipe M.G. França, Diego L.C. Dutra
    Neurocomputing, 2023
  • Lotka-Volterra Applied to Misinformation Extinction in Opportunistic Networks
    Victor Messner, Anderson Zudio, Diego Dutra, Claudio Amorim
    Lecture Notes in Networks and Systems, 2023
  • A Comparative Study of Fishing Activity Detection Approaches in Maritime Surveillance
    Vinicius D. Do Nascimento, Tiago A. O. Alves, Diego L. C. Dutra, Sandip Kundu
    Proceedings 2023 Congress in Computer Science Computer Engineering and Applied Computing Csce 2023, 2023
  • Dendrite-inspired Computing to Improve Resilience of Neural Networks to Faults in Emerging Memory Technologies
    Lizy K. John, Felipe M. G. França, Subhasish Mitra, Zachary Susskind, Priscila M. V. Lima, Igor D. S. Miranda, Eugene B. John, Diego L. C. Dutra, Mauricio Breternitz
    2023 IEEE International Conference on Rebooting Computing Icrc 2023, 2023
  • COIN: Combinational Intelligent Networks
    Igor D. S. Miranda, Aman Arora, Zachary Susskind, Josias S. A. Souza, Mugdha P. Jadhao, Luis A. Q. Villon, Diego L. C. Dutra, Priscila M. V. Lima, Felipe M. G. França, Mauricio Breternitz, Lizy K. John
    Proceedings of the International Conference on Application Specific Systems Architectures and Processors, 2023
  • Toward Enabling Network Slice Mobility to Support 6G System
    Miloud Bagaa, Diego Leonel Cadette Dutra, Tarik Taleb, Hannu Flinck
    IEEE Transactions on Wireless Communications, 2022
  • Weightless Neural Networks for Efficient Edge Inference
    Zachary Susskind, Aman Arora, Igor D. S. Miranda, Luis A. Q. Villon, Rafael F. Katopodis, Leandro S. de Araújo, Diego L. C. Dutra, Priscila M. V. Lima, Felipe M. G. França, Mauricio Breternitz, Lizy K. John
    Parallel Architectures and Compilation Techniques Conference Proceedings Pact, 2022
  • Machine learning based fast self optimized and life cycle management network
    Abdelhakim Nacef, Abdellah Kaci, Youcef Aklouf, Diego Leonel Cadette Dutra
    Computer Networks, 2022
  • AI-Based Network-Aware Service Function Chain Migration in 5G and beyond Networks
    Rami Akrem Addad, Diego Leonel Cadette Dutra, Tarik Taleb, Hannu Flinck
    IEEE Transactions on Network and Service Management, 2022
  • Distributive Thermometer: A New Unary Encoding for Weightless Neural Networks
    Alan T. L. Bacellar, Zachary Susskind, Luis A. Q. Villon, Igor D. S. Miranda, Leandro Santiago de Araújo, Diego Leonel Cadette Dutra, Mauricio Breternitz Jr., LIZY JOHN, Priscila Lima, Felipe França
    Esann 2022 Proceedings 30th European Symposium on Artificial Neural Networks Computational Intelligence and Machine Learning, 2022
  • LogicWiSARD: Memoryless Synthesis of Weightless Neural Networks
    Igor D.S. Miranda, Aman Arora, Zachary Susskind, Luis A.Q. Villon, Rafael F. Katopodis, Diego L.C. Dutra, Leandro S. De Araujo, Priscila M.V. Lima, Felipe M.G. Franca, Lizy K. John, Mauricio Breternitz
    Proceedings of the International Conference on Application Specific Systems Architectures and Processors, 2022
  • A New Interest-Based Protocol to Hide and Protect Servers in IP Networks
    Marco A. Coutinho, Evandro L. C. Macedo, Luis F. M. de Moraes, Victor Cracel Messner, Diego L. Cadette Dutra, Claudio L. de Amorim, Valeriana Gomes Roncero, Nilton Alves Juinior, Marita Maestrelli, Marcio Portes de Albuquerque, Sandro Silva
    International Conference on Information Networking, 2022
  • Toward Using Reinforcement Learning for Trigger Selection in Network Slice Mobility
    Rami Akrem Addad, Diego Leonel Cadette Dutra, Tarik Taleb, Hannu Flinck
    IEEE Journal on Selected Areas in Communications, 2021
  • Self-optimized network: When Machine Learning Meets Optimization
    Abdelhakim Nacef, Miloud Bagaa, Youcef Aklouf, Abdellah Kaci, Diego Leonel Cadette Dutra, Adlen Ksentini
    Proceedings IEEE Global Communications Conference Globecom, 2021
  • On SDN-Driven Network Optimization and QoS Aware Routing Using Multiple Paths
    Miloud Bagaa, Diego Leonel Cadette Dutra, Tarik Taleb, Konstantinos Samdanis
    IEEE Transactions on Wireless Communications, 2020
  • Optimization model for cross-domain network slices in 5g networks
    Rami Akrem Addad, Miloud Bagaa, Tarik Taleb, Diego Leonel Cadette Dutra, Hannu Flinck
    IEEE Transactions on Mobile Computing, 2020
  • Workshop 15: MPP Parallel Programming Models - Special Edition Machine Learning Performance and Security
    Leandro A. J. Marzulo, Tiago A. O. Alves, Cristiana Bentes, Gabriele Mencagli
    Proceedings 2020 IEEE 34th International Parallel and Distributed Processing Symposium Workshops Ipdpsw 2020, 2020
  • Fast Service Migration in 5G Trends and Scenarios
    Rami Akrem Addad, Diego Leonel Cadette Dutra, Miloud Bagaa, Tarik Taleb, Hannu Flinck
    IEEE Network, 2020
  • Network Slice Mobility in Next Generation Mobile Systems: Challenges and Potential Solutions
    Rami A. Addad, Tarik Taleb, Hannu Flinck, Miloud Bagaa, Diego Dutra
    IEEE Network, 2020
  • Fluid computing: Interest-based communication in dataflow/multiset rewriting computing
    Rui Rodrigues de Mello Junior, Leandro Santiago de Araújo, Diego Leonel Cadette Dutra, Gabriel Antoine Louis Paillard, Cláudio Luis de Amorim, Felipe Maia Galvão França
    ACM International Conference Proceeding Series, 2020
  • Towards studying service function chain migration patterns in 5G networks and beyond
    Rami Akrem Addad, Diego Leonel Cadette Dutra, Miloud Bagaa, Tarik Taleb, Hannu Flinck
    Proceedings IEEE Global Communications Conference Globecom, 2019
  • Towards a Fast Service Migration in 5G
    Rami Akrem Addad, Diego Leonel Cadette Dutra, Miloud Bagaa, Tarik Taleb, Hannu Flinck
    2018 IEEE Conference on Standards for Communications and Networking Cscn 2018, 2018
  • Virtual security as a service for 5G verticals
    Yacine Khettab, Miloud Bagaa, Diego Leonel Cadette Dutra, Tarik Taleb, Nassima Toumi
    IEEE Wireless Communications and Networking Conference Wcnc, 2018
  • Towards Modeling Cross-Domain Network Slices for 5G
    Rami Akrem Addad, Tarik Taleb, Miloud Bagaa, Diego Leonel Cadette Dutra, Hannu Flinck
    Proceedings IEEE Global Communications Conference Globecom, 2018
  • MIRA!: An SDN-Based Framework for Cross-Domain Fast Migration of Ultra-Low Latency 5G Services
    Rami Akrem Addad, Diego Leonel Cadette Dutra, Tarik Taleb, Miloud Bagaa, Hannu Flinck
    Proceedings IEEE Global Communications Conference Globecom, 2018
  • Benchmarking the ONOS Intent Interfaces to Ease 5G Service Management
    Rami Akrem Addad, Diego Leonel Cadette Dutra, Miloud Bagaa, Tarik Taleb, Hannu Flinck, Mehdi Namane
    Proceedings IEEE Global Communications Conference Globecom, 2018
  • An efficient virtual system clock for the wireless raspberry pi computer platform
    Diego L. C. Dutra, Edilson C. Corrêa, Claudio L. Amorim
    Concurrency and Computation Practice and Experience, 2017
  • Ensuring End-to-End QoS Based on Multi-Paths Routing Using SDN Technology
    Diego Leonel Cadette Dutra, Miloud Bagaa, Tarik Taleb, Konstantinos Samdanis
    Proceedings IEEE Global Communications Conference Globecom, 2017
  • RadFlow: An Interest-Centric Task Based Dataflow Runtime
    Diego L.C. Dutra, Heberte F. Moraes, Claudio L. Amorim
    Proceedings Symposium on Computer Architecture and High Performance Computing, 2016
  • Attaining strictly increasing and precise time count in energy-efficient computer systems
    Diego Leonel Cadette Dutra, Lauro Luis Armondi Whately, Claudio Luis de Amorim
    Proceedings Symposium on Computer Architecture and High Performance Computing, 2013

RECENT SCHOLAR PUBLICATIONS

  • Weightless Neural Networks on Flexible Substrates: A Novel Approach to Wearable Machine Learning
    IDS Miranda, V Pillai, T Musale, M Jadhao, PCRS Neto, Z Susskind, ...
    IEEE Transactions on Very Large Scale Integration (VLSI) Systems 34 (2), 444-452 , 2025
    2025
  • Updating KernelCanvas for weightless graph classification
    RB Barbosa, DLC Dutra, PMV Lima, D Carvalho, FMG França
    Neurocomputing 646, 130458 , 2025
    2025
    Citations: 1
  • Analyzing Offshore Vessel Encounters: A Dataset for Enhancing Maritime Security and Monitoring
    VD do Nascimento, CM de Farias, DLC Dutra, TAO Alves
    2025 28th International Conference on Information Fusion (FUSION), 1-8 , 2025
    2025
  • Edge-Optimized Weightless Neural Network for Low-Power Wearable Arrhythmia Detection
    V Pillai, S Nag, M Jadhao, A Bacellar, I Miranda, F França, D Dutra, ...
    IEEE , 2025
    2025
    Citations: 1
  • arrWNN: Arrhythmia-detecting weightless neural network FlexIC
    V Pillai, IDS Miranda, T Musale, M Jadhao, PCRS Neto, Z Susskind, ...
    2024 IEEE International Flexible Electronics Technology Conference (IFETC), 1-4 , 2024
    2024
    Citations: 10
  • A hybrid framework for maritime surveillance: Detecting illegal activities through vessel behaviors and expert rules fusion
    VD do Nascimento, TAO Alves, CM de Farias, DLC Dutra
    Sensors 24 (17), 5623 , 2024
    2024
    Citations: 24
  • LogicNets vs. ULEEN: Comparing two novel high throughput edge ML inference techniques on FPGA
    S Nag, Z Susskind, A Arora, ATL Bacellar, DLC Dutra, IDS Miranda, ...
    2024 IEEE 67th International Midwest Symposium on Circuits and Systems … , 2024
    2024
    Citations: 3
  • Ensemble learning approaches for detecting fishing activity in maritime surveillance: A performance evaluation
    VD Do Nascimento, CM De Farias, DLC Dutra, TAO Alves
    2024 27th International Conference on Information Fusion (FUSION), 1-8 , 2024
    2024
    Citations: 3
  • Uleen: A novel architecture for ultra-low-energy edge neural networks
    Z Susskind, A Arora, IDS Miranda, ATL Bacellar, LAQ Villon, ...
    ACM Transactions on Architecture and Code Optimization 20 (4), 1-24 , 2023
    2023
    Citations: 31
  • Dendrite-inspired computing to improve resilience of neural networks to faults in emerging memory technologies
    LK John, FMG França, S Mitra, Z Susskind, PMV Lima, IDS Miranda, ...
    2023 IEEE International Conference on Rebooting Computing (ICRC), 1-5 , 2023
    2023
  • A conditional branch predictor based on weightless neural networks
    LAQ Villon, Z Susskind, ATL Bacellar, IDS Miranda, LS de Araújo, ...
    Neurocomputing 555, 126637 , 2023
    2023
    Citations: 8
  • A Comparative Study of Fishing Activity Detection Approaches in Maritime Surveillance
    VD Do Nascimento, TAO Alves, DLC Dutra, S Kundu
    2023 Congress in Computer Science, Computer Engineering, & Applied Computing … , 2023
    2023
    Citations: 3
  • COIN: Combinational intelligent networks
    IDS Miranda, A Arora, Z Susskind, JSA Souza, MP Jadhao, LAQ Villon, ...
    2023 IEEE 34th International Conference on Application-specific Systems … , 2023
    2023
    Citations: 5
  • Lotka-Volterra Applied to Misinformation Extinction in Opportunistic Networks
    V Messner, A Zudio, D Dutra, C Amorim
    International Conference on Advanced Information Networking and Applications … , 2023
    2023
  • An FPGA-Based Weightless Neural Network for Edge Network Intrusion Detection.
    Z Susskind, A Arora, ATL Bacellar, DLC Dutra, IDS Miranda, M Breternitz, ...
    FPGA, 232 , 2023
    2023
    Citations: 2
  • ULEEN: A Novel Architecture for Ultra Low-Energy Edge Neural Networks
    LS DE ARAÚJO, DLC DUTRA, PMV LIMA, FMG FRANÇA, ...
    2023
  • Weightless neural networks for efficient edge inference
    Z Susskind, A Arora, IDS Miranda, LAQ Villon, RF Katopodis, ...
    Proceedings of the International Conference on Parallel Architectures and … , 2022
    2022
    Citations: 53
  • Reverse branch target buffer poisoning
    JLNC de Oliviera, DLC Dutra
    Relatório Technico ES-783/22, Universidade Federal do Rio de Janeiro , 2022
    2022
    Citations: 3
  • LogicWiSARD: Memoryless synthesis of weightless neural networks
    IDS Miranda, A Arora, Z Susskind, LAQ Villon, RF Katopodis, DLC Dutra, ...
    2022 IEEE 33rd International conference on application-specific systems … , 2022
    2022
    Citations: 25
  • Toward enabling network slice mobility to support 6G system
    M Bagaa, DLC Dutra, T Taleb, H Flinck
    IEEE Transactions on Wireless Communications 21 (12), 10130-10144 , 2022
    2022
    Citations: 24

MOST CITED SCHOLAR PUBLICATIONS

  • Optimization model for cross-domain network slices in 5G networks
    RA Addad, M Bagaa, T Taleb, DLC Dutra, H Flinck
    IEEE Transactions on Mobile Computing 19 (5), 1156-1169 , 2019
    2019
    Citations: 111
  • Network slice mobility in next generation mobile systems: Challenges and potential solutions
    RA Addad, T Taleb, H Flinck, M Bagaa, D Dutra
    IEEE Network 34 (1), 84-93 , 2020
    2020
    Citations: 88
  • Fast service migration in 5G trends and scenarios
    RA Addad, DLC Dutra, M Bagaa, T Taleb, H Flinck
    IEEE Network 34 (2), 92-98 , 2020
    2020
    Citations: 72
  • Ensuring end-to-end QoS based on multi-paths routing using SDN technology
    DLC Dutra, M Bagaa, T Taleb, K Samdanis
    GLOBECOM 2017-2017 IEEE Global Communications Conference, 1-6 , 2017
    2017
    Citations: 66
  • On SDN-driven network optimization and QoS aware routing using multiple paths
    M Bagaa, DLC Dutra, T Taleb, K Samdanis
    IEEE Transactions on Wireless Communications 19 (7), 4700-4714 , 2020
    2020
    Citations: 65
  • Virtual security as a service for 5G verticals
    Y Khettab, M Bagaa, DLC Dutra, T Taleb, N Toumi
    2018 IEEE Wireless Communications and Networking Conference (WCNC), 1-6 , 2018
    2018
    Citations: 60
  • Weightless neural networks for efficient edge inference
    Z Susskind, A Arora, IDS Miranda, LAQ Villon, RF Katopodis, ...
    Proceedings of the International Conference on Parallel Architectures and … , 2022
    2022
    Citations: 53
  • AI-based network-aware service function chain migration in 5G and beyond networks
    RA Addad, DLC Dutra, T Taleb, H Flinck
    IEEE Transactions on Network and Service Management 19 (1), 472-484 , 2021
    2021
    Citations: 45
  • Toward using reinforcement learning for trigger selection in network slice mobility
    RA Addad, DLC Dutra, T Taleb, H Flinck
    IEEE Journal on Selected Areas in Communications 39 (7), 2241-2253 , 2021
    2021
    Citations: 43
  • Towards a fast service migration in 5G
    RA Addad, DLC Dutra, M Bagaa, T Taleb, H Flinck
    2018 IEEE conference on standards for communications and networking (CSCN), 1-6 , 2018
    2018
    Citations: 43
  • Towards modeling cross-domain network slices for 5G
    RA Addad, T Taleb, M Bagaa, DLC Dutra, H Flinck
    2018 IEEE Global communications conference (GLOBECOM), 1-7 , 2018
    2018
    Citations: 35
  • Uleen: A novel architecture for ultra-low-energy edge neural networks
    Z Susskind, A Arora, IDS Miranda, ATL Bacellar, LAQ Villon, ...
    ACM Transactions on Architecture and Code Optimization 20 (4), 1-24 , 2023
    2023
    Citations: 31
  • Benchmarking the ONOS intent interfaces to ease 5G service management
    RA Addad, DLC Dutra, M Bagaa, T Taleb, H Flinck, M Namane
    2018 IEEE Global Communications Conference (GLOBECOM), 1-6 , 2018
    2018
    Citations: 27
  • LogicWiSARD: Memoryless synthesis of weightless neural networks
    IDS Miranda, A Arora, Z Susskind, LAQ Villon, RF Katopodis, DLC Dutra, ...
    2022 IEEE 33rd International conference on application-specific systems … , 2022
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    Citations: 25
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  • Toward enabling network slice mobility to support 6G system
    M Bagaa, DLC Dutra, T Taleb, H Flinck
    IEEE Transactions on Wireless Communications 21 (12), 10130-10144 , 2022
    2022
    Citations: 24
  • Towards studying service function chain migration patterns in 5G networks and beyond
    RA Addad, DLC Dutra, M Bagaa, T Taleb, H Flinck
    2019 IEEE Global Communications Conference (GLOBECOM), 1-6 , 2019
    2019
    Citations: 15
  • MIRA!: An SDN-based framework for cross-domain fast migration of ultra-low latency 5G services
    RA Addad, DLC Dutra, T Taleb, M Bagaa, H Flinck
    2018 IEEE Global Communications Conference (GLOBECOM), 1-6 , 2018
    2018
    Citations: 15
  • Distributive Thermometer: A New Unary Encoding for Weightless Neural Networks.
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    ESANN , 2022
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