Zilverling

A. Yousefzadeh PhD (Amir)

Assistant Professor
EEMCS-CS-CAES
Zilverling 5090
a.yousefzadeh@utwente.nl
+31534897988
Business card

I joined the University of Twente in February 2024 as an Assistant Professor in edge AI. My focus is on designing Low Power AI processors and neuromorphic systems.  


Brief CV:

Expertise

  • Computer Science

    • Neural Network
    • Computer Hardware
    • Energy Efficient
    • Artificial Intelligence
    • Electronic Learning

  • Engineering

    • Internet-Of-Things
    • Artificial Neural Network
    • Sensor System

Organisations

My research interests:

  • Neuromorphic Sensing and Processing
  • Low Power / Scalable AI processes
  • Hardware-aware algorithm design

I extensively use digital hardware design tools and gate-level simulations to co-optimize hardware architectures with neural network algorithms. Some of my technical experiences:

  • Design of programmable neuromorphic processors for embedded AI applications
  • On-device learning algorithms and hardware accelerators
  • Bio-inspired vision processing
  • Benchmarking and comparison of various algorithm optimizations in hardware

Looking for PhD or Postdoc positions: please check the UTwente career website. All the positions are advertised there, and applications should be submitted online. Please avoid sending your application via email.

Looking for master thesis or internship (UTwente students only): Please send me an email with your CV and grades (both bachelor and master). You can look at this document to learn the types of assignments and required skills. Also, look into the past projects. 

Looking for master thesis or internship (external student): You should look into the Exchange Student Program at UTwente. Once you're accepted into this program, I would be happy to host you. 

Publications

2025

Spike-based neuromorphic computing: An overview from bio-inspiration to hardware architectures and learning mechanisms (2025)Microprocessors and microsystems. Article 105240 (E-pub ahead of print/First online). Gebregiorgis, A., Yousefzadeh, A., Eissa, S., Siddiqi, M. A., Frenkel, C., Zenke, F., Bohte, S., Mahmoud, A. N., Das, A., Hamdioui, S., Corporaal, H. & Corradi, F.https://doi.org/10.1016/j.micpro.2025.105240Editorial: Algorithm-hardware co-optimization in neuromorphic computing for efficient AI (2025)Frontiers in Neuroscience, 19, 01-03. Article 1746610. Yousefzadeh, A., Patiño-Saucedo, A., De Croon, G. & Sifalakis, M.https://doi.org/10.3389/fnins.2025.1746610From RISC-V Cores to Neuromorphic Arrays: A Tutorial on Building Scalable Digital Neuromorphic Processors (2025)[Working paper › Preprint]. ArXiv.org. Yousefzadeh, A.https://doi.org/10.48550/arXiv.2512.00113Overcoming the Limitations of Layer Synchronization in Spiking Neural Networks (2025)[Working paper › Preprint]. ArXiv.org. Koopman, R., Yousefzadeh, A., Shahsavari, M., Tang, G. & Sifalakis, M.https://doi.org/10.48550/arXiv.2408.05098TrackCore-F: Deploying Transformer-Based Subatomic Particle Tracking on FPGAs (2025)[Working paper › Preprint]. ArXiv.org. Blankestijn, A., Odyurt, U. & Yousefzadeh, A.https://doi.org/10.48550/arXiv.2509.26335Memory Wall is not gone: A Critical Outlook on Memory Architecture in Digital Neuromorphic Computing (2025)In IEEE Computer Society Annual Symposium on VLSI, ISVLSI 2025 - Conference Proceedings (Proceedings of IEEE Computer Society Annual Symposium on VLSI, ISVLSI). IEEE. Yousefzadeh, A., Sohail, S. & Varbanescu, A. L.https://doi.org/10.1109/ISVLSI65124.2025.11130262Event-based optical flow on neuromorphic processor: ANN vs. SNN comparison based on activation sparsification (2025)Neural networks, 188. Article 107447. Xu, Y., Tang, G., Yousefzadeh, A., de Croon, G. C. H. E. & Sifalakis, M.https://doi.org/10.1016/j.neunet.2025.107447Sparse Convolutional Recurrent Learning for Efficient Event-based Neuromorphic Object Detection (2025)[Working paper › Preprint]. ArXiv.org. Wang, S., Xu, Y., Yousefzadeh, A., Eissa, S., Corporaal, H., Corradi, F. & Tang, G.https://doi.org/10.48550/arXiv.2506.13440Senmap: Multi-objective data-flow mapping and synthesis for hybrid scalable neuromorphic systems (2025)[Working paper › Preprint]. ArXiv.org. Nembhani, P. V., Rhodes, O., Tang, G., Dobrita, A. F., Xu, Y., Vadivel, K., Shidqi, K., Detterer, P., Konijnenburg, M., van Schaik, G.-J., Sifalakis, M., Al-Ars, Z. & Yousefzadeh, A.https://doi.org/10.48550/arXiv.2506.03450Efficient Synaptic Delay Implementation in Digital Event-Driven AI Accelerators (2025)[Working paper › Preprint]. ArXiv.org. Meijer, R., Detterer, P., Yousefzadeh, A., Patino-Saucedo, A., Tang, G., Vadivel, K., Xu, Y., Gomony, M.-D., Corradi, F., Linares-Barranco, B. & Sifalakis, M.https://doi.org/10.48550/arXiv.2501.13610

Research profiles

Funded projects: 

  1. NeAIxt (2025): Next Generation of edge AI crossing technology fields
  2. TIRAMISU(2024): Training and Innovation in Reliable and Efficient Chip Design for Edge AI
  3. NEUROKIT2E(2023): Open-source deep learning platform dedicated to Embedded hardware and Europe
  4. REBECCA(2023): Reconfigurable Heterogeneous Highly Parallel Processing Platform for safe and secure AI
  5. NimbleAI(2022): Ultra-energy efficient and secure neuromorphic sensing and processing at the endpoint

PhD Students: 

  1. Sameed Sohail(2024-Present): Embedded Neuromorphic Processor Architecture with On-Device Adaptation

Master students: 

  1. Kaushik Narayan (2026): Compressed sensing for neuromorphic processor pre-processing (with Innatera)
  2. Mohammed Huzaifa (2026): Accelerator design for learning in neuromorphic processors
  3. Sjoerd van der Plaat (2026): Optimizing off-chip memory access through dynamic inference, applied on Jetson Nano.
  4. Stefan den Haan (2026): Asynchronous inference on SparkRV neuromorphic processor
  5. Lennart Jensen (2026): Improving reliability of neuromorphic processor for space applications
  6. Peter Kingma (2026): Error correction for a fast asynchronous neuromorphic link
  7. Ada Dotsika (2026): Analog implementation of GRU unit for keyword spotting applications
  8. Douwe Vries (2026): Post-synthesis power optimization for digital neuromorphic processors
  9. Pratik Phadte (2026): Near sensor processing for smart lighting application (with Signify)
  10. Arjan Blankestijn(2026): Accelerating Transformers on ZynQ platforms (work in progress conference presentation)
  11. Belal Elshinnawey (2026): Embedded FPGA design for neuromorphic processors
  12. Bram Bremer(2025, cum-laude): Real-time acoustic imaging on an FPGA using recurrent neural networks
  13. Mattias Westerink(2025, cum-laude): Designing co-processor for RISC-V-based neuromorphic system
  14. Wiebren Wijnstra(2025): Optimizing RISC-V processors for neuromorphic workloads
  15. Wim Nijsink(2025): Measuring the reliability of existing neuromorphic solutions
  16. Sharon Moolenaar(2025): Optimizing Network on chip for neuromorphic processors
  17. Haoran Wolfgang(2024): Low-latency hardware accelerator for sparse convolutional recurrent network toward neuromorphic object detection
  18. Ivan Knunyants(2024): Optimizing transformer neural networks for event-driven inference in hardware
  19. Yashwanth Gopinath(2024): Open-source RISC-V-based neuromorphic processor
  20. Roel Koopman (2023): Overcoming the Limitations of Layer Synchronization in Spiking Neural Networks
  21. Cina Arjmand (2023): Trainable Region of Interest Prediction: Hard Attention Framework for Hardware-Efficient Event-Based Computer Vision Neural Networks on Neuromorphic Processors  
  22. Lucas Huijbregts (2023): Transposable Multiport SRAM-based In-Memory Compute Engine for Binary Spiking Neural Networks in 3nm FinFET
  23. Shenqi Wang (2023): Hardware Efficient Object Detection for High Spatial Resolution Event Camera
  24. Refik Can Bilgiç (2023):  Analytical Modelling of 3D System Partitioning
  25. Pietro Martinello (2023): Forging a Multimodal Dataset: Uniting Diverse Sensor Data for Enhanced Analysis
  26. Roy Meijer (2023): Efficient Synaptic Delay Implementation in Digital Event-Driven Neuromorphic Processors
  27. Kevin Shidqi (2022): Benchmarking and Algorithm Optimization for SENeCA, a RISC-V-based Neuromorphic Processor
  28. Alexandra-Florentina Dobrit (2022): Brain-inspired feature extraction for near sensor extreme edge processing with Spiking Neural Networks
  29. Prithvish Vijaykumar Nembhani (2022): Efficient mapping of large-scale SNN and rate-based DNN on SENeCA
  30. Preetha Vijayan (2021): Temporal Delta Layer: Exploiting Temporal Sparsity in Deep Neural Networks for Time-Series Data

Bachelor students:

  1. Emiel de Vries (2026): LLM-assisted RTL generation for a pretrained deep neural network
  2. Bas Reterink (2025): Person detection using an event-based camera on STM32
  3. Pierluigi Gatt(2025): Person detection using an event-based camera on STM32
  4. Mattijn Spitteler(2025): Improving the analogue section of IO extenders for a hydraulic cylinder controller

Visiting students:

  1. Mohamed Saber (MSc, 2026): Nano LLMs mapping for neuromorphic systems
  2. Harinandanan Ajith Maya (BSc, 2026): ChipLet-based neuromorphic processing platform
  3. Omar Mansour (MSc, 2025): NERVE: A Neuromorphic Vision and Radar Ensemble for Multi-Sensor Fusion Research (FAIR Data Fund project)
  4. Ege Tan (BSc, 2025): neuromorphic processor external communication 
  5. Ethan Milon(MSc, 2024): Radar processing for smart office applications
  6. Mustafa Canitz(BSc, 2024): Event-based camera processing for smart office applications

Address

University of Twente

Zilverling (building no. 11), room 5090
Hallenweg 19
7522 NH Enschede
Netherlands

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