Research Topic:

Hardware-software co-design for FPGA-based deep learning acceleration, AI compiler frameworks, and sparsity-aware NPU architectures.

Supervisors:

Prof. Christian Plessl (PC2)

Thesis Abstract:

This thesis pursues two parallel thrusts in hardware-software co-design for FPGA-based AI acceleration. The first matures an AI compiler framework toward production-grade reliability and broader model support, extending operator coverage across convolutional, transformer, and post-transformer architectures. The second designs a sparsity-aware mesh-of-tiles Neural Processing Unit in VHDL, prototyped on the OTUS FPGA cluster and validated to Post-PnR. Both converge in a unified benchmark comparing compiler-generated dataflow, hand-coded RTL, the custom NPU, and commercial NPUs.

Publications:

•Egor Trushin, Raviraj Mandalia, and Andreas Görling. “Analyzing the Response of Exchange–Correlation Potentials of Chain-like Molecules to Electric Fields by Kohn–Sham Inversion and Evaluation of the Response within the Random Phase Approximation”. In: The Journal of Chemical

Physics 163.24 (Dec. 2025), p. 244115. issn: 0021-9606, 1089-7690. doi: 10.1063/5.0294687.

•Thalles M. Moreira, Fábio D. L. Coutinho, and Arnaldo S. R. Oliveira. “AI Frameworks and DL Processing Units Performance Evaluation for CNN-Based Real-Time RF Modulation Classification”. In: Proceedings of the 15th International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies. HEART ’25. ACM. Kumamoto, Japan: ACM Press, 2025, pp. 147–155

Conference Presentations:

•HEART 2025, International Symposium on Highly Efficient Accelerators and Reconfigurable Technologies, Kumamoto, Japan, May 2025.