Personal profile
Research profile
Sherif Eissa is a PhD Candidate under the supervision of prof. Henk Corporaal and prof. Sander Stuijk in the Electronic Systems group of the Department of Electrical Engineering at Eindhoven University of Technology (TU/e). His PhD work is part of a national research project "efficientdeeplearning.nl".
In his project, Sherif looks to unvail the power of neuromorphic computing for efficient real-time AI through hardware design.
Academic background
Sherif earned his Bachelor cum laude in Information Engineering with a major in Electronics in 2016 from German University in Cairo, earning his bachelor thesis at the Institute for Microelectronics Stuttgart (IMS) and University of Stuttgart. He continued to earn his Masters degree cum laude in Information technology and Embedded Systems in 2019 from University of Stuttgart where his Master's thesis at Bosch Research Campus, Renningen discussed CNN accelerators and sparsity utilization. In both bachelor and masters, Sherif was recognized and awarded as the best achieving student in his class in overall grades.
Sherif's research interests intersect Machine learning, hardware design and data encoding. He is intreseted in innovating parallel data processing structures with innovated memory structures and sparsity as a key component to low power edge AI.
Expertise related to UN Sustainable Development Goals
In 2015, UN member states agreed to 17 global Sustainable Development Goals (SDGs) to end poverty, protect the planet and ensure prosperity for all. This person’s work contributes towards the following SDG(s):
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SDG 7 Affordable and Clean Energy
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Collaborations and top research areas from the last five years
Projects
- 1 Finished
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TTW P16-25 (project 7): Efficient Deep Learning Platforms (eDLP)
Stuijk, S. (Project Manager), Eissa, S. (Project member), van der Hagen, D. (Project communication officer) & de Mol-Regels, M. (Project communication officer)
1/09/18 → 31/12/23
Project: Research direct
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Spike-based neuromorphic computing: An overview from bio-inspiration to hardware architectures and learning mechanisms
Gebregiorgis, A. (Corresponding author), Yousefzadeh, A., Eissa, S., Siddiqi, M. A., Frenkel, C., Zenke, F., Bohte, S., Mahmoud, A. N., Das, A., Hamdioui, S., Corporaal, H. & Corradi, F. (Corresponding author), 20 Dec 2026, (Accepted/In press) In: Microprocessors and Microsystems. XX, 105240.Research output: Contribution to journal › Article › Academic › peer-review
Open Access -
NEXUS: A 28nm 3.3pJ/SOP 16-Core Spiking Neural Network with a Diamond Topology for Real-Time Data Processing
Sadeghi, M., Rezaeiyan, Y., Khatiboun, D. F., Eissa, S., Corradi, F., Augustine, C. & Moradi, F., Jun 2025, In: IEEE Transactions on Biomedical Circuits and Systems. 19, 3, p. 523-535 13 p., 10661301.Research output: Contribution to journal › Article › Academic › peer-review
Open AccessFile4 Link opens in a new tab Citations (Scopus)8 Downloads (Pure) -
POQ: Is There a Pareto-Optimal Quantization Strategy for Deep Neural Networks?
de Putter, F. (Corresponding author), Eissa, S. & Corporaal, H., 2025, In: IEEE Access. 13, p. 81434-81449 16 p., 10988610.Research output: Contribution to journal › Article › Academic › peer-review
Open AccessFile1 Link opens in a new tab Citation (Scopus) -
Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection
Wang, S., Xu, Y., Yousefzadeh, A., Eissa, S., Corporaal, H., Corradi, F. & Tang, G., 14 Nov 2025, 2025 International Joint Conference on Neural Networks, IJCNN 2025. Institute of Electrical and Electronics Engineers, 8 p. 11229261Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
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STEMS: Spatial-Temporal Mapping for Spiking Neural Networks
Eissa, S. (Corresponding author), Stuijk, S., de Putter, F., Nardi-Dei, A., Corradi, F. & Corporaal, H., Sept 2025, In: IEEE Transactions on Computers. 74, 9, p. 2991-3002 12 p., 11059316.Research output: Contribution to journal › Article › Academic › peer-review
Open AccessFile1 Link opens in a new tab Citation (Scopus)3 Downloads (Pure)
Datasets
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Aircraft Marshaling Signals Dataset of FMCW Radar and Event-Based Camera for Sensor Fusion
Müller, L. (Creator), Sifalakis, M. (Creator), Eissa, S. (Creator), Yousefzadeh, A. (Creator), Stuijk, S. (Creator), Corradi, F. (Creator) & Detterer, P. (Creator), Zenodo, 1 May 2023
DOI: 10.5281/zenodo.7656911, https://zenodo.org/records/10359770
Dataset