BEN ABDALLAH Abderazek
Professor, Regent (Dean of the Undergraduate school)
- Affiliation
- Department of Computer Science and Engineering/Division of Computer Engineering
- Title
- Professor, Regent (Dean of the Undergraduate school)
- benab@u-aizu.ac.jp
Education
- Courses - Undergraduate
- Computer Architecture, Undergraduate level, UoA, 2018 – present
- Introduction to Computer Systems, Undergraduate level, UoA, 2018 – present
- Parallel Computer Systems, Undergraduate level, UoA, 2018 – present
- Computer System Engineering, UoA, 2008-2018
- Embedded Systems, UoA, 2008-2016
- Logic Circuit Design Exercises, UoA, 2008-2016
- Courses - Graduate
- Neuromorphic Computing, UoA, 2023 – present
- Embedded Real-Time Systems, UoA, 2008 – 2022
- Multicore Computing, UoA, 2010 – 2015
- Advanced Computer Organization, UoA, 2008 – 2023
Research
- Specialization
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Computer system
- Educational Background, Biography
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Education and Academic Career:
- 2002 Doctor of Engineering (Dr. Eng.) in Computer Engineering, University of Electro-Communications, Tokyo, Japan
- 2002.4–2007.3 Research Associate, National University of Electro-Communications, Tokyo
- 2007.4–2007.9 Assistant Professor, National University of Electro-Communications, Tokyo
- 2007.10–2011.3 Assistant Professor, University of Aizu (UoA)
- 2011.4–2012.3 Associate Professor, UoA
- 2012.4–2014.3 Senior Associate Professor, UoA
- 2014.4–Present Professor, UoA
- 2014.4–2022.3 Head, Computer Engineering Division, UoA
- 2014.4–Present Member, Education and Research Council, UoA
- 2022.4–Present Director, Department of Computer Science and Engineering, UoA
- 2022.4–Present Dean, School of Computer Science and Engineering, UoA
- 2022.4–Present Regent, University of Aizu
Invited Lecturer:- 2010–2013 Visiting Professor, Department of Computer Science and Engineering, Hong Kong University of Science and Technology, Hong Kong, China
- 2011–2015 Visiting Professor, School of Software Engineering, Huazhong University of Science and Technology, Wuhan, China
- 2022–2025 Lecturer, Graduate School of Science and Technology, Kyoto Institute of Technology, Kyoto, Japan
- 2023–Present Lecturer, Tokyo University of Foreign Studies, Tokyo, Japan
- Current Research Theme
Abderazek Ben Abdallah's research is dedicated to advancing energy-efficient, high-performance computing systems, with a particular emphasis on digital signal processing workloads constrained by computational, network, and reliability challenges. His work in neuromorphic AI circuits and systems encompasses energy-efficient learning algorithms and AI-chip hardware development, driving advancements in low-power computing, adaptive intelligence and real-time processing for embedded applications. He has also contributed to the advancement of on-chip interconnects, particularly fault-tolerant 3D-NoCs/ICs (SiPh, Hybrid), designed to optimize system efficiency. Additionally, his research enhances the reliability of integrated circuits and SoCs by tackling key challenges like thermal management and error mitigation. His algorithms and systems have been instrumental in advancing numerous emerging applications and have been patented across multiple innovations through collaborations with various industrial partners.
- Key Topic
- Neuromorphic Chips; Neuromorphic Circuits and Systems; On-chip Interconnects (2D/3D, Si-Photonics, Hybrid); Emerging Applications; Low-power Computing, Adaptive Intelligence; Real-time; Sustainable; Anthropomorphic Robots
- Affiliated Academic Society
- Professional Memberships and Honors:
- Elected Member, IEEE CASS Technical Committee on Circuits and Systems Education and Outreach (CASEO), 2025–
- Full Member, Sigma Xi (Class of 2025)
- IEEE Senior Member (since 2014)
- ACM Senior Member (since 2016)
Major Editorial:- Associate Editor-in-Chief, IEEE Computer Magazine (Flagship Publication of the IEEE Computer Society), 2026–present
- Associate Editor, IEEE Computer Magazine, 2025–present
- Associate Editor, IEEE Network Magazine, 2025–2027
Main research
- Adaptive Distributed Autonomous Systems
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We investigate next‑generation adaptive distributed autonomous systems through the lens of anthropomorphic prosthetics, androids, and intelligent robotic platforms. Our research integrates cutting‑edge neuroscience, artificial intelligence, neuromorphic computing, and robotics to create highly responsive, lifelike systems capable of operating autonomously while adapting to human intent and dynamic environments.
Leveraging neuromorphic architectures and spiking neural networks, we develop control frameworks that enable natural, intuitive interaction between artificial limbs, androids, and biological systems. These brain‑inspired models support real‑time adaptation, low‑power operation, and seamless communication across distributed components.
Our work on non‑invasive neural interfaces allows prosthetic devices to adjust continuously to user intent, improving precision, comfort, and fluidity of motion. In parallel, our research on advanced sensory processing equips androids with human‑like perceptual capabilities, enabling them to interpret complex environmental stimuli, collaborate with humans, and function autonomously within distributed multi‑agent settings.
By bridging biomechanical engineering with AI‑driven cognition, we are advancing assistive technologies, human augmentation, and adaptive robotics. Our efforts extend to distributed anthropomorphic androids, where multiple embodied agents coordinate intelligently, share sensory information, and adapt collectively to real‑world tasks. This work lays the foundation for autonomous systems that are deeply integrated into daily life, scalable across environments, and capable of evolving with human needs.
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- Advanced of On-Chip Interconnects
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Complex SoCs contain dozens of components made of processor cores, DSPs, memory, accelerators, and I/O, all integrated into a single die area of just a few square millimeters. Such complex systems will be interconnected via a complex on-chip interconnect closer to a sophisticated network than current bus-based solutions. This network must provide high throughput and low latency while keeping area and power consumption low. Our research effort is about solving several design challenges to enable such new paradigm in massively parallel many-core systems. In particular, we are investigating fault-tolerance, 3D-TSV integration, photonic communication, low-power mapping techniques, and low-latency adaptive routing.
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- Brain‑Inspired & Neuromorphic Computing
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We are exploring the development of an adaptive ultra-low power neuromorphic chip (NASH) and systems, enhanced by our previously developed fault-tolerant three-dimensional on-chip interconnect technology. The NASH system boasts several features, including an efficient adaptive configuration method that enables the reconfiguration of various SNN parameters such as spike weights, routing, hidden layers, and topology. Additionally, the system incorporates a blend of different deep neural network topologies, an efficient fault-tolerant multicast spike routing algorithm, and an effective on-chip learning mechanism. To demonstrate the performance of the NASH system, we will develop an FPGA implementation and establish a VLSI implementation. The ultimate goal of NASH is to bring brain-inspired processing technology to small-scale embedded sensors and sensor-based devices, such as BCI (EEG/EMG), audio, presence detection, and activity recognition.
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- Embedded Systems & Software–Hardware Codesign
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Our research in power and energy-efficient computing systems is essential to meeting the growing demand for more powerful and sustainable technology. As society increasingly relies on computing devices, managing their energy consumption becomes crucial. By developing efficient computing systems, we can significantly reduce energy costs, minimize environmental impact, and extend the battery life of portable devices. In large-scale data centers, enhancing energy efficiency leads to substantial cost savings and a reduced carbon footprint. Our work in this field fosters innovation in hardware and software design, paving the way for smarter, greener technologies that benefit both users and the planet.
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- Sustainable Computing: World's first AI-Enabled off-grid energy storage solar carport
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Our research is dedicated to the design and utilization of computers with minimal environmental impact, encompassing efforts to reduce energy consumption, minimize waste, and employ sustainable materials. By integrating cutting-edge technologies and innovative methodologies, we aim to develop solutions that not only enhance the efficiency and functionality of computing systems but also contribute to the preservation of our planet. Our multidisciplinary approach involves collaboration with companies and experts in various fields, ensuring that our findings and implementations are both practical and impactful.
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