Research
Ongoing
Real-time environmental awareness using FPGA-based machine learning and sensor fusion in millimeter wave networks (Project: 6GEM; Funded by BMBF)
This research concentrates on developing AI-aided beam management algorithms for mmWave networks employing multimodal sensor fusion. The main goal is to implement the beam management algorithms on FPGA for real-time inference focusing on reduced latency and resource allocation.
FPGA design for Network Security (Project: ESCALATE; Funded by FWO and SNSF))
This work concentrates on the acceleration of large flow detection algorithms on FPGAs. An algorithm-architecture co-design approach is followed to develop novel algorithms and implementations for detecting data flows that only exceed the allowed bandwidth to a limited extent. Probabilistic data structures and approximate computing are employed to develop hardware-efficient architectures for large flow detection targeting Terabit Ethernet. The goal is to integrate the configurable hardware in network devices and demonstrate efficient and effective protection against large flow network attacks in high-speed networks.
Past
Project: Automatic Design Synthesis and Optimization of Machine Learning Algorithms on Resource-constrained Devices(Funded by MOE Singapore)
The main aim of this project was to Optimize deep learning algorithms, especially vision-based neural networks, both on algorithmic and hardware level and develop an automated tool for hardware-efficient implementation on FPGAs using Python and Verilog.