果冻影院

XClose

Institute of Communications and Connected Systems

Home
Menu

Dr Adnan Mehonic

Profile picture of Adnan Mehonic

Lecturer in Nanoelectronics and Royal Academy of Engineering Research Fellow

E:
T:听听+44 (0)20 3108 1116 (int. 51116)

Research group

Power-efficient ML hardware | memristive technology | neuromorphic engineering

Biography听

Dr Mehonic received a BSc in Electronic Engineering from the University of Sarajevo in 2009 and was awarded the Golden Badge, the best student award. He graduated from 果冻影院 (果冻影院) with an MSc in Nanotechnology (Distinction, Oxford Instruments prize for the best MSc project) in 2010 and PhD in 2014 (top 3 best PhD thesis in 2013/14, EE Department), demonstrating the first ambient operating all-SiOx memristor. He has been working as a Research Associate in the group of Electronic Materials and Devices, EEE 果冻影院 till 2017, further developing silicon oxide memristive technology.听 In 2017, he was awarded a highly prestigious 5-year Royal Academy of Engineering Research Fellowship to work on neuromorphic technology for energy-efficient AI hardware. In 2019, he was appointed as a Lecturer in Nanoelectronics. He serves on the advisory boards of Wiley鈥檚 Adv. Intelligent Systems and is an Editor for Frontiers in Materials and Frontiers in Nanotechnology. He is a board member for IoP鈥檚 Dielectrics and Electrostatics group, and an IoP and IET member. At the EEE department, he is the director of the MSc in Nanotechnology.

To date, he has authored more than 40 journal publications and over 60 international conference proceedings (including more than ten invited talks). His research resulted in two major EPSRC project grants - EP/K01739X/1 in 2013 and EP/P013503/1 in 2016, and a Leverhulme grant in 2016 , and the RAEng Research Fellowship in 2017. He is the inventor of 5 resistance-switching patents and co-funder of spinout company (鈥淚ntrinSic Semiconductor Technology鈥), where he serves as a Chief Technology Officer. He received the 鈥淥ne to Watch 2015鈥 award from 果冻影院 Enterprise for 果冻影院鈥檚 most innovative staff.

His current work is focused on energy-efficient nanoelectronics and functional materials. More specifically, he works on non-von Neumann computing paradigms harnessing the physics of memristive devices to perform both memory and computing. He is interested in circuits and algorithms for on-chip implementation of ML/AI and nonconventional information processing algorithms (e.g. spike-based computing).

Publications

RPS Widget Placeholder