Meet the team

Charlotte Frenkel received the M.Sc. degree (summa cum laude) in Electromechanical Engineering and the Ph.D. degree in Engineering Science from Université catholique de Louvain (UCLouvain), Louvain-la-Neuve, Belgium in 2015 and 2020, respectively. In February 2020, she joined the Institute of Neuroinformatics, UZH and ETH Zurich, Switzerland, as a postdoctoral researcher. Since July 2022, she is an Assistant Professor at Delft University of Technology, The Netherlands.

Her research aims at bridging the bottom-up (bio-inspired) and top-down (engineering-driven) design approaches toward neuromorphic intelligence, with a focus on hardware accelerators for (Neuro)AI, and brain-inspired on-device learning.

Dr. Frenkel received a best paper award at the IEEE International Symposium on Circuits and Systems (ISCAS) 2020 conference, and her Ph.D. thesis was awarded the FNRS / Nokia Bell Scientific Award 2021 and the FNRS / IBM Innovation Award 2021. In 2023, she was awarded prestigious AiNed Fellowship and Veni grants from the Dutch Research Council (NWO). She presented several invited talks, including keynotes at the tinyML EMEA technical forum 2021 and at the Neuro-Inspired Computational Elements (NICE) neuromorphic conference 2021. She serves as a program co-chair of the NICE conference since 2023 and of the tinyML Research Symposium 2024, as a TPC member of IEEE ESSERC, and as an associate editor for the IEEE Transactions on Biomedical Circuits and Systems. She is a co-lead of the NeuroBench initiative for fair benchmarks in neuromorphic computing.

Email: c.frenkel at tudelft.nl

Dr. Adrian Kneip received the Master's and Ph.D. degree in electrical engineering from the Université catholique de Louvain in 2019 and 2024, respectively, where he specialized in the fields of electronic circuits and systems. He is now a postdoctoral research fellow, working in collaboration between TU Delft (NL) and KULeuven (BE).

His research interests notably include the design of ultra-low-power digital ICs, as well as analog/mixed-signal accelerators for edge-AI chips. He has a particular interest for SRAM-based in-memory computing and hardware/software co-design. Recently, Dr. Kneip led several chip designs in research teams and is the author or co-author of several research papers in IEEE conferences and journals, receiving the Best Student Paper Award for the 2022's ESSCIRC/ESSDERC conference. He also serves as reviewer for top IEEE journals, and was student branch representative to the Benelux Section from 2020 to 2023.

Email: a.kneip at tudelft.nl

Martin Lefebvre received the M.Sc. degree (summa cum laude) in Electromechanical Engineering and the Ph.D. degree in Engineering Sciences and Technology from the Université catholique de Louvain (UCLouvain), Louvain-la-Neuve, Belgium, in 2017 and 2024, respectively. He pursued his Ph.D thesis, focusing on area-efficient and temperature-independent current references for the Internet of Things, under the supervision of Prof. David Bol, and simultaneously served as a teaching assistant in Electrical Engineering. He also participated to eight tape-outs in various technology nodes. His current research interests include hardware-aware machine learning algorithms, low-power mixed-signal vision chips for embedded image processing, and ultra-low-power current reference architectures. He serves as a reviewer for various IEEE journals and conferences including Journal of Solid-State Circuits (JSSC), Transactions on Circuits and Systems I and II (TCAS-I and TCAS-II), Transactions on Biomedical Circuits and Systems (TBioCas), Transactions on VLSI Systems (TVLSI), and International Symposium on Circuits and Systems (ISCAS).

Davide was born on 8th December 1999 in Italy. He completed his BSc degree in Computer Engineering at Università di Modena e Reggio Emilia (UNIMORE), located in Italy, 2021. Two years later, in 2023, he obtained an MSc degree (Summa Cum Laude) at the University of Lugano (USI), located in Switzerland, in Artificial Intelligence. For his MSc., Davide worked in the area of optimization algorithms and data science, focusing on federated and self-supervised learning in his thesis. In November 2023, he moved to TUDelft to pursue his Ph.D. under the joint supervision of Dr. Charlotte Frenkel and Dr. Justin Dauwels. His research is currently focusing on factor graphs, predictive coding, bio-plausible learning rules, and neuromorphic computing.

If you are interested in any of these topics, feel free to reach out for collaborations or to discuss ideas.

Email: d.casnici at tudelft.nl

Dennis did both his bachelors and masters degree in physics with a minor in computer science at the Johannes Gutenberg University in Mainz, Germany. During his bachelor’s degree he pursued theoretical physics as his main interest and switched to a focus on experimental particle physics during his master. His master thesis was done as part of the ATLAS experiment located at CERN and included high-speed link tests, PCB design and ultra-low latency machine learning applications on FPGAs. During this time he discovered his true passion, namely machine intelligence with a strong hardware component.

He is currently enrolled as a PhD student at TUDelft where he works on combining the latest findings in theoretical and experimental neuroscience with state-of-the-art machine learning techniques to realise hardware-efficient cortical microcircuits. His main research interests include human and machine intelligence with a strong hardware component.

Email: d.layh at tudelft.nl

Hey! My name is Douwe den Blanken and I am PhD student in the Cognitive Sensor Systems and Nodes group. I completed my bachelor's degree in Aerospace Engineering (cum laude) here in Delft, followed by a masters in Embedded Systems (cum laude, honors). Throughout my masters, I focused on deep learning (but also vision and RL), custom machine learning accelerator hardware and high-performance (accelerated) computing. For my thesis, under the supervision of Dr. Charlotte Frenkel, I designed and taped out a chip called 'Chameleon' that can do on-chip few-shot learning.

Continuing this, in 2023, I started my PhD in the Cognitive Sensor Nodes and Systems group of Dr. Frenkel. Currently, my research interests are:

• Transformers & transformer acceleration
• ASIC/ML accelerator design
• Edge computing/real-life deployments/tinyML (Extreme) neural network quantization
• Meta-learning/continual learning/unsupervised learning + synergies with each of these
• Open-source ML software and hardware (currently have 22 public repositories and counting!)

Email: d.m.j.denblanken at tudelft.nl

Guilherme Guedes received his B.Sc and M.Sc degree in Electrical Engineering from Faculdade de Engenharia da Universidade do Porto (FEUP), Porto, Portugal, in 2021 and 2023, respectively. In February 2024, he started his doctoral studies at Delft University of Technology's EEMCS faculty, on the Microelectronics department. His current research focuses on Lifelong Learning, and how current approaches can be improved and implemented on edge devices to allow smart-devices to learn on-the-go.

Email: g.guedes at tudelft.nl

Nicolas was born on 20th August 1998 in Arlon, Belgium. He completed his BSc degree (Magna Cum Laude) in Electrical and Software Engineering at Université Catholique de Louvain (UCLouvain), located in Belgium, in 2019. In 2021, he obtained an MSc degree (Summa Cum Laude) in the same university in Electrical Engineering with a focus on electronic circuits and systems. For his MSc., Nicolas worked in the area of digital in-memory computing (IMC) and spiking neural networks (SNNs) targeting ultra-low power consumption accelerators for devices at the edge. He started his Ph.D. at UCLouvain after the end of his studies, a period during which he did a three-month internship at Yale University in the research group of Prof. Rajit Manohar to learn about asynchronous design. He moved in July 2021 to TUDelft to pursue his research with Dr. Charlotte Frenkel as his supervisor. He is currently continuing his work done during his MSc thesis with an additional focus on ultra-low latency for critical applications. Nicolas is passionate about nature, lizards, and beers.

Email: n.chauvaux at tudelft.nl