Ahmed Hemani

Ahmed Hemani is a professor of Electronic Systems Design at KTH Royal Institute of Technology, specializing in energy-efficient custom computing machines and design automation. His research covers a broad range of topics, including massively parallel architectures, custom computing frameworks like SiLago (Silicon Lego), and innovations in VLSI design, particularly in post-Moore’s Law computing.

He has extensive industry experience, having worked at companies such as National Semiconductors, ABB, Ericsson, and Philips Semiconductors. His contributions have significantly advanced areas like Networks-on-Chip, globally asynchronous and locally synchronous designs, and power management architectures. In addition, he has been part of several start-ups and continues to focus on cutting-edge technologies like memristor-based computing.

Dimitrios Stathis

Dimitrios Stathis is an engineer and researcher specializing in neural networks and brain-inspired computing architectures. He holds a PhD in embedded systems from KTH Royal Institute of Technology, earned in 2022, and has master’s degrees in microelectronics and computer systems, as well as embedded systems. His work focuses on developing energy-efficient systems for artificial neural networks, addressing challenges related to computational loads and energy consumption in large-scale systems. He has contributed significantly to research on fault-tolerant and learning-capable architectures. He is currently a postdoc under the supervision of Prof. Hemani.

Yu Yang

I am currently a postdoc in KTH. My research interests are EDA tool development for novel parallel architectures and system design automation. I received my BSc degree from Sichuan University and MSc degree from Politecnico di Torino. In 2022, I received my PhD from KTH in the field of electronic systems.

Jordi Altayó

I am PhD student at KTH Royal Institute of Technology. I received my bachelor’s degree in electrical engineering from (UPC Barcelona)[https://telecos.upc.edu/en] and my master’s degree in embedded systems from KTH. My research focuses on HPC and edge device architectures for energy and power efficiency, as well as advanced designed methodologies in front and back-end ASIC flows

Saba Yousefzadeh

Saba Yousefzadeh is a Ph.D. student at KTH Royal Institute of Technology in the School of Electrical Engineering and Computer Science. She began working on the APROPOS project in 2021, focusing on approximate computing strategies for industrial and scientific applications. Saba has a B.Sc. in Electrical Engineering from Iran University of Science and Technology (2017) and an M.Sc. in Digital Systems from the University of Tehran (2021).

Her research interests span energy-efficient computing, neuromorphic computing, machine learning, and high-level synthesis. In her master’s thesis, she developed a reconfigurable accelerator for biomedical applications, which resulted in multiple conference papers. Currently, Saba collaborates with industry partners like Ericsson on projects involving algorithms such as BCPNN and Self-Organizing Maps.

Ritika Ratnu

PhD ,,,

Maah Paara

PhD …