Basic Information

Affiliation
Adaptive Systems Laboratory
Title
Professor, Head of the Computer Engineering Division
E-Mail
benab@u-aizu.ac.jp
Web site
http://www.u-aizu.ac.jp/~benab/

Education

Courses - Undergraduate
- Computer Architecture
- Introduction to Computer Systems
- Parallel Computer Systems
Courses - Graduate
- Advanced Computer Organization
- Embedded Real-Time Systems

Research

Specialization
Computer Systems
Educational Background, Biography
Education
  • 6/1994 B.S., Electrical Engineering, Huazhong University of Science and Technolgy (HUST)
  • 6/1997 M.S., Computer Engineering, Huazhong University of Science and Technology (HUST)
  • 3/2002 Ph.D., Computer Engineering, University of Electro-communications (UEC), Tokyo
Career
  • 4/2002-3/2007 Research Associate, The University of Electro-Communications (UEC), Tokyo
  • 4/2007-9/2007 Assistant Professor, The University of Electro-Communications (UEC), Tokyo
  • 10/2007-3/2011 Assistant Professor, The University of Aizu (UoA)
  • 2010-2013 Visiting Professor, Hong Kong University of Science and Technology (KUST)
  • 4/2011-3/2012 Associate Professor, The University of Aizu (UoA)
  • 2011-2015 Visiting Professor, Huazhong University of Science and Technology (HUST)
  • 4/2012-3/2014 Senior Associate Professor, The University of Aizu (UoA)
  • 4/2014-present Professor, The University of Aizu (UoA)
  • 4/2014-present Head of the Computer Engineering Division, The University of Aizu (UoA)
Current Research Theme
Key Topic
Brain-inspired Computing; Spike-based Neural Network Dynamics; Spike-based Learning;
Cybernetics Prostheses; Machine Learning Systems
Affiliated Academic Society
IEEE Senior Member; ACM Senior Member

Others

Hobbies
Reading and visiting historical places
School days' Dream
To become a school teacher!
Current Dream
Motto
Simple is the best!
Favorite Books
" You Can Heal Your Life " 
Messages for Students
Concentration and organization are the keys to the success of your education and research.
Publications other than one's areas of specialization

Main research

Robust Algorithms, Architectures and Efficient Learning Strategies for Heterogeneous and Sparse Data

Deep Neural Networks have shown tremendous progress in many real-world applications (i.e., object recognition, autonomous vehicles, etc.). To improve data processing systems' performance, designers use large-scale models on dedicated hardware platforms such as FPGAs, GPUs, or ASICs. Designers need a long time to collect datasets, train, and design accelerators to keep the trained models private. However, with the growing complexity of DL acceleration, there are severe vulnerabilities in these AI accelerators' hardware implementations. An attacker who does not know the details of structures and designs inside these accelerators can effectively reverse engineer the neural networks by leveraging various side-channel information. Our goal is to study and develop resilient algorithms and hardware for robust trustworthy Edge-AI computing systems for various emerging applications (i.e., Edge, IoT, NoV).

Related publications.

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Neuromorphic Systems

We aim to understand the role of spike-based learning in brain-inspired systems considering various constraints that are not usually taken into account in simulations, such as the effect of variability in the neural network parameters or the impact of bounded weights in the learning/training phase. Our mid-term goal is to develop an innovative cognitive brain-inspired system that can effectively run in parallel with energy efficiency on sequence processing tasks (i.e., a stream of events from sensors), produce intelligent behavior, interact, and adapt to the environment.
Currently, we are researching robust algorithms, architectures, and digital implementations targeting specific applications of spiking neural networks in adaptive control of prosthetic robotic arms and sensory processing applications. For a proof of concept, we prototyped a reliable three-dimensional digital neuromorphic system geared explicitly toward the 3D-ICs biological brain's three-dimensional structure (R-NASH), where information in the network is represented by sparse patterns of spike timing and learning is based on the local spike-timing-dependent plasticity rule. R-NASH enables real-time and low-power solutions targeted at full-custom manycore system-on-chip integration.
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Cybernetics Prostheses with Sensorimotor Integration

Computational models enable the investigation of the underlying mechanisms for neural control and adaptive biological processes. To realize energy-efficient real-time devices, neuromorphic systems are the core of next-generation systems for brain repair. Besides developing the neuromorphic circuit and system components, our main focus here is constructing and analyzing neuromorphic agents (i.e., hand, robot arm) integrated into cybernetic chains of action, in which nervous systems engage in closed interaction with their bodies and environments. Our current goal is to investigate novel adaptive neural prosthetic/robotic hands based on biological signal discrimination with spiking neural networks to restore hand function movement for people with amputations or neurological disorders.

Related publications.

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Dissertation and Published Works

A complete list of publications is available here.

Some recent selected publications are included below:

[1] Abderazek Ben Abdallah, Khanh N. Dang,"Toward Robust Cognitive 3D Brain-inspired Cross-paradigm System," Frontiers in Neuroscience, 6/2021 (in press), doi: 10.3389/fnins.2021.690208

[2] Mark Ogbodo, Khanh N. Dang, Abderazek Ben Abdallah,"On the Design of a Fault-tolerant Scalable Three Dimensional NoC-based Digital Neuromorphic System with On-chip Learning," IEEE Access, 4/2021, DOI: 10.1109/ACCESS.2021.3071089

[3] Khanh N. Dang, Akram Ben Ahmed, Abderazek Ben Abdallah, Xuan-Tu Tran, "HotCluster: A thermal-aware defect recovery method for Through-Silicon-Vias Towards Reliable 3-D ICs systems," IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems March 2021. DOI: 10.1109/TCAD.2021.3069370

[4] Book: Abderazek Ben Abdallah (Author),"Advanced Multicore Systems On-Chip: Architecture, On-Chip Network, Design”, Publishers: Springer; 1st ed, 2017, ISBN-13: 978-9811060915, ISBN-10: 98110609162017.

[5] The H. Vu,Yuichi Okuyama, Abderazek Ben Abdallah, "Comprehensive Analytic Performance Assessment and K-means based Multicast Routing Algorithms and Architecture for 3D-NoC of Spiking Neurons," ACM Journal on Emerging Technologies in Computing Systems (JETC), Vol. 15, No. 4, Article 34, October 2019. doi: 10.1145/3340963

[6] K. N. Dang, Akram Ben Ahmed, Yuichi Okuyama, Abderazek Ben Abdallah,"Scalable design methodology and online algorithm for TSV-cluster defects recovery in highly reliable 3D-NoC systems," IEEE Transactions on Emerging Topics in Computing (TETC), IEEE, Volume 8, Issue 3, pp 577-590, 2020.

[7] K. N. Dang, A. B. Ahmed, A. Ben Abdallah and X. Tran, "TSV-OCT: A Scalable Online Multiple-TSV Defects Localization for Real-Time 3-D-IC Systems," IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 28, no. 3, pp. 672-685, 3/2020. doi: 10.1109/TVLSI.2019.2948878.

[9] Khanh N. Dang, Akram Ben Ahmed, Xuan-Tu Tran, Yuichi Okuyama, Abderazek Ben Abdallah, "A Comprehensive Reliability Assessment of Fault-Resilient Network-on-Chip Using Analytical Model," IEEE Transactions on Very Large Scale Integration (VLSI) Systems, Vol. 25, Issue: 11, pp. 3099 – 3112, vol. 2017. DOI: 10.1109/TVLSI.2017.2736004

[10] Khanh N. Dang, Akram Ben Ahmed, Yuichi Okuyama, Abderazek Ben Abdallah, "Scalable Design Methodology and Online Algorithm for TSV-cluster Defects Recovery in Highly Reliable 3D-NoC Systems," IEEE Transactions on Emerging Topics in Computing, Vol:8, Issue: 3, pp. 577-590, 2020. DOI: 10.1109/TETC.2017.2762407