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DANG Nam Khanh

Associate Professor

Affiliation
Department of Computer Science and Engineering/Division of Computer Engineering
Title
Associate Professor
E-Mail
khanh@u-aizu.ac.jp
Web site
https://u-aizu.ac.jp/~khanh/

Education

Courses - Undergraduate
PL03: JAVA Programming I (Ex.), University of Aizu, Undergraduate, Q1.
FU05: Computer Architecture (Ex.), University of Aizu, Undergraduate, Q1.
SE08: Introduction of Big Data Analytics (Ex.), University of Aizu, Undergraduate, Q3.
FU06: Operating Systems (Ex.), University of Aizu, Undergraduate, Q4.
Courses - Graduate
SYA14: Neuromorphic Computing, University of Aizu, Postgraduate, Q2.

Research

Specialization
Control and system engineering
Electron device and electronic equipment
Computer system
High performance computing
Computational science
Intelligent informatics
- Neuromorphic Computing
- Machine Learning
- Fault-tolerance
- Generative AI
Educational Background, Biography
Educational Background
- Ph.D. in Computer Science and Engineering, The University of Aizu, Japan, 2017
- M.Sc. in Information Systems & Technology, University of Paris-XI, France, 2014
- B.Sc. in Electronics & Telecommunications, VNU University of Engineering and Technology, Vietnam, 2011

Work Experience
- Associate Professor, The University of Aizu, 2022 April - now.
- Assistant Professor, VNU University of Engineering and Technology, Vietnam National University, Hanoi, 2017 November - 2022 March.
- Visiting Researcher, The University of Aizu, 2020 November - 2021 March.
- Visiting Researcher, The University of Aizu, 2019 May - 2019 September.
- Researcher, SISLAB, Vietnam National University, Hanoi, 2011-2014.
- RTL Designer, Dolphin Vietnam Inc., 2010-2011.
Current Research Theme
- Neuromorphic engineering
- Generative AI
Key Topic
Neuromorphic Computing, Faul-tolerance, VLSI, 3D Integrated Circuits, Generative AI
Affiliated Academic Society

Others

Hobbies
Football, Chess,Analog Photography
School days' Dream
Be a person that can change the world!
Current Dream
Be a good teacher :)
Motto
Fundamentals Are Everything :)
Favorite Books
The Fountainhead - Ayn Rand
Messages for Students
I strongly believe in learning fundamentals before doing anything advanced. Please spend some time to read, study, and practice basic topics like mathematics, English and programing. I will certainly help you in a long career ahead. You need to build a strong foundation, not a house of card. You can be slower than your peers for now, but can move forward way futher with good fundanmentals understanding. Life is not sprint, it's a marathon.

Main research

Brain-inspired Algorithms and Systems

Brain-inspired (Neuromorphic) computing uses spiking neuron network models to solve machine learning problems in a more power/energy-efficient way when compared to the conventional artificial neural networks.We research adaptive low-power spiking neuromorphic systems and SoCs empowered with our earlier developed fault-tolerant three-dimensional on-chip interconnect technology. In particular, we investigate innovative algorithms and neuromorphic systems, including adaptive configuration methods to enable the reconfiguration of different network parameters (spike weights, routing, hidden layers, topology, etc.), fault-tolerance, thermal-aware mapping methods, and on-line learning algorithms. The target applications include anthropomorphic robotics and edge computing.


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Power/Energy-Efficient Computing Architectures and Systems/SoCs

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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Dissertation and Published Works

Patent:

- A. Ben Abdallah, Khanh N. Dang, An on-chip 3D system in which TSV groups containing multiple TSVs connect layers together [複数のTSVを含むTSVグループが層間を接続するオンチップの3次元システム], 特許第7488989号, Japan patent, [Certificate], [Google Patent].

- A. Ben Abdallah, Khanh N. Dang, Masayuki Hisada, TSV Error Tolerant Router Device for 3D Network On Chip, 特許第7239099号, Japan patent,

Selected Publication:

- Ngo-Doanh Nguyen, Akram Ben Ahmed, Abderazek Ben Abdallah, Khanh N. Dang, “Power-aware Neuromorphic Architecture with Partial Voltage Scaling 3D Stacking Synaptic Memory“, IEEE Transactions on Very Large Scale Integration Systems (TVLSI), accepted, 2023.


- Ngo-Doanh Nguyen, Xuan-Tu Tran, Abderazek Ben Abdallah, Khanh N. Dang, “An In-situ Dynamic Quantization with 3D Stacking Synaptic Memory for Power-aware Neuromorphic Architecture”, IEEE Access, vol. 11, pp. 82377-82389, 2023.

- Khanh N. Dang, Nguyen Anh Vu Doan, Abderazek Ben Abdallah “MigSpike: A Migration Based Algorithm and Architecture for Scalable Robust Neuromorphic Systems”, IEEE Transactions on Emerging Topics in Computing (TETC), [DOI: 10.1109/TETC.2021.3136028].

- 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, IEEE, Volume 41, No. 4, pp. 799-812, April 2022. [DOI: 10.1109/TCAD.2021.3069370].

- Khanh N. Dang, Akram Ben Ahmed, Ben Abdallah Abderrazak and Xuan-Tu Tran, “TSV-OCT: A Scalable Online Multiple-TSV Defects Localization for Real-Time 3-D-IC Systems”, IEEE Transactions on Very Large Scale Integration Systems (TVLSI), IEEE, Volume 28, Issue 3, pp. 672 - 685, 2020. [DOI: 10.1109/TVLSI.2019.2948878].

- 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 (TETC), IEEE, Volume 8, Issue 3, pp. 577-590, 2020. [DOI: 10.1109/TETC.2017.2762407].

- 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 Systems (TVLSI), IEEE, Volume 25, Issue 11, pp. 3099-3112, 2017. [DOI: 10.1109/TVLSI.2017.2736004].


More details: https://u-aizu.ac.jp/~khanh/