"Combination of Approximate Computing and Approximate Stacking Memory for Low-Power Neuromorphic Computing", Principal Investigator, funded by UoA Competitive Research Funding, No. P24-2024 (2024-2025).
"Low-Power Spiking Neural Network Solution for IoT and Edge Devices", Principal Investigator, funded by UoA Competitive Research Funding, No. P26-2023 (2023-2024).
"Hotspot-Aware Fault-Tolerant Architectures and Algorithms for TSV-Based 3D Network-on-Chips", Principal Investigator, funded by National Foundation for Science and Technology Development (NAFOSTED), No. 102.01-2018.312 (2019-2021).
"Soft Error Resilient Architecture and Algorithm for Network-on-Chip", Principal Investigator, funded by VNU University of Engineering and Technology (VNU-UET), No. CN18.10 (2018-2019).
"Development of IoT Dual-Band Transmitters for Agriculture (IOTA)", Core Member, funded by the Ministry of Science and Technology (World Bank Project) (2018-2019).
"Reconfiguration Solutions in Network-on-Chip Architectures", Core Member, funded by NAFOSTED, No. 102.01-2013.17 (2014-2016).
"Investigation, Design, and Implementation of a Video Encoder for Next-Generation Multimedia Equipment", Core Member, funded by Vietnam National University, Hanoi (VNU), No. QGĐA.10.02 (2010-2013).
Awards
Second Prize in the Vietnamese Nhan Tai Dat Viet Award 2015. Awarded for our VENGME H.264/AVC encoding chip, for which I was part of the design team.
Best Student Paper Award at the International Symposium on Ubiquitous Networking (UNet 2021) for the paper:
Ogbodo Mark Ikechukwu, Khanh N. Dang, and Abderazek Ben. Abdallah, "Energy-Efficient Spike-Based Scalable Architecture for Next-Generation Cognitive AI Computing Systems". [Certificate]
Best Paper Award at the 2023 IEEE 6th International Conference on Electronics Technology (ICET) for the paper:
Jiangkun Wang, Khanh N. Dang, and Abderazek Ben Abdallah, "Scaling Deep-Learning Pneumonia Detection Inference on a Reconfigurable Self-Contained Hardware Platform".
