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
    • Artificial Intelligence
    • Energy Efficient
    • Electronic Learning
    • Edge AI
    • Multicore

  • Engineering

    • Artificial Neural Network

Organisations

My research interests:

  • Neuromorphic Sensing and processing
  • Low Power AI processos
  • 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. 

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

Memory 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.2501.13610Explore Activation Sparsity in Recurrent LLMs for Energy-Efficient Neuromorphic Computing (2025)[Working paper › Preprint]. ArXiv.org. Knunyants, I., Tavakol, M., Sifalakis, M., Xu, Y., Yousefzadeh, A. & Tang, G.https://doi.org/10.48550/arXiv.2501.16337

2024

EON-1: A Brain-Inspired Processor for Near-Sensor Extreme Edge Online Feature Extraction (2024)IEEE Transactions on Circuits and Systems for Artificial Intelligence, 1(2), 128-140. Article 10744412. Dobrita, A., Yousefzadeh, A., Thorpe, S., Vadivel, K., Detterer, P., Tang, G., Schaik, G.-J. v., Konijnenburg, M., Gebregiorgis, A., Hamdioui, S. & Sifalakis, M.https://doi.org/10.1109/TCASAI.2024.3491673Energy-efficient SNN Architecture using 3nm FinFET Multiport SRAM-based CIM with Online Learning (2024)In Proceedings of the 61st ACM/IEEE Design Automation Conference, DAC 2024 (pp. 1-6). Article 260 (Proceedings - Design Automation Conference). IEEE. Huijbregts, L., Liu, H. H., Detterer, P., Hamdioui, S., Yousefzadeh, A. & Bishnoi, R.https://doi.org/10.1145/3649329.3656514Invited: Neuromorphic Vision Modalities in the NimbleAI 3D Chip (2024)In Proceedings of the 61st ACM/IEEE Design Automation Conference, DAC 2024 (pp. 1-4). Article 358 (Proceedings - Design Automation Conference). IEEE. Iturbe, X., Linares-Barranco, B., Ieng, S. H., Erdmann, A., Peres, L., Rhodes, O., Tornero, R., Sifalakis, M., Van De Burgwal, M., Yousefzadeh, A., Kooli, M., Alidori, R. & Zaykov, P.https://doi.org/10.1145/3649329.3689622Multidie 3-D Stacking of Memory Dominated Neuromorphic Architectures (2024)IEEE transactions on very large scale integration (VLSI) systems, 32(11), 2144-2148. Giacomini Rocha, L. M., Bilgic, R., Naeim, M., Das, S., Oprins, H., Yousefzadeh, A., Konijnenburg, M., Milojevic, D., Myers, J., Ryckaert, J. & Biswas, D.https://doi.org/10.1109/TVLSI.2024.3421625

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

Bachelor students:

  1. Bas Reterink (2025): Person detection using an event-based camera on STM32
  2. Pierluigi Gatt(2025): Person detection using an event-based camera on STM32
  3. Mattijn Spitteler(2025): Improving the analogue section of IO extenders for a hydraulic cylinder controller

Visiting students:

  1. Ege Tan (2025): neuromorphic processor external communication 
  2. Ethan Milon(2024): Radar processing for smart office applications
  3. Mustafa Canitz(2024): Event-based camera processing for smart office applications
  4. YingFu Xu (2022):  Implementation of bio-inspired Optimical flow algorithm in neuromorphic processor
  5. Alberto Patino-Saucedo (2022): Hardware-aware training of models with synaptic delays for digital event-driven neuromorphic processors

Address

University of Twente

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

Navigate to location

Organisations

Scan the QR code or
Download vCard