Alexantrou Serb graduated from Imperial college in 2009 as a biomedical engineer and later obtained his PhD from Imperial college in 2013 in electronics engineering. Since then he has been a research fellow in Prof. Prodromakis' group, working on memristors technology characterisation and application development. His research interests include circuit design, computational neuroscience and biology-machine interfaces. He was appointed Reader at Univ of Edinburgh in May 2022.
One of the greatest challenges of current research is how to embody the level of intelligence found in the brain into a physical computer that operates with the energy efficiency of the brain. To that end, algorithmics, computer architecture design, nanotechnology and ultimately also bio-interfacing must work together, as no isolated approach can deliver this vision. Throughout project FORTE this researcher will be involved in: 1) developing new applications enabled by enmeshing memristors into existing electronics technologies and 2) understanding how the availability of memristive technologies can unlock new capabilities in artificial intelligence systems.The applications developed in this programme will span from simple, modular micro-circuits employing several memristors to larger scale systems utilising thousands of devices. Examples of the former would include reconfigurable logic (digital) and filter (analogue) blocks that can be aggregated into much larger computational systems much like Lego pieces can be used to construct complicated superstructures. Examples of the latter would include functional blocks that can carry out probabilistic inference, biosignal processing or neutral network-type inference.The algorithmics part of the research is oriented towards the 'problem of multiplication': multiplication is expensive to perform accurately in hardware. Memristive technologies, however, offer an opportunity to implement not only multiplication, but other basic mathematical operations such as scalar matching in hardware at very low energy. Furthermore, reducing energy budgets for such operations opens the way towards more powerful, more intelligent AI.
Dr Shiwei Wang is an Associate Professor in Centre for Electronics Frontiers (CEF), School of Electronics and Computer Science. He received his Bachelor degree in Electronic Engineering (Outstanding Honor) from Zhejiang University, China in 2010 and PhD degree in Microelectronics from University of Edinburgh, UK in 2014. Following his PhD research, he was with SIAT, Chinese Academy of Sciences, China as a Research Assistant Professor before moving to IMEC, Belgium where he worked as a Senior Researcher till December 2020. At IMEC he took technical lead in several projects to develop ultra-low-power integrated circuits for neural interfacing applications. His research at IMEC has led to innovative breakthroughs in high density neural probe and integrated brain machine interface technologies. He is a senior member of IEEE and has over 30 publications including premier journals and conferences such as Science, JSSC, TBCAS, ISSCC, VLSI, IEDM, etc.
He has main research expertise on analogue & mixed signal integrated circuits, with particular interest in circuits for AI, biomedical, implantable/wearable, brain machine interface, sensor instrumentation applications.
Spyros obtained his Applied Physics diploma in 2009 and his MSc in Microelectronics and Nanotechnology in 2011, both from the National Technical University of Athens (NTUA). In 2015 he received his PhD from the Department of Physics, NTUA for his work on the effects of infrared laser annealing in the electrical characteristics of silicon and germanium. He joined the University of Southampton, UK in 2016.
Spyros is currently working on the fabrication, characterisation and application of metal oxide memristive devices.
Sachin Maheshwari is a Research Associate at the Centre for Electronics Frontiers in the School of Engineering at the University of Edinburgh, Scotland. Prior to that, he was a Research Fellow in the Department of Electronics and Computer Science at the University of Southampton. He did his PhD in the Applied DSP and VLSI Research Group at the University of Westminster, London, UK. His doctoral thesis was focused on the Adiabatic Approach for Low-Power Passive NFC systems. He did his Master of Engineering Degree in Microelectronics from Birla Institute of Technology and Science (BITS), Pilani, India and Bachelor of Technology Degree in Electrical and Electronics Engineering from ICFAI Tech, Hyderabad, India. In the past, he was also a Lecturer in the EEE Department at BITS Pilani for over 4 years.
His research interests include Artificial Neural Networks and Neuromorphic Computing, across energy recovery logic (adiabatic technique) and emerging technology (RRAM) for developing brain-inspired energy-efficient systems.
Georgios received his M.Eng. degree in electrical and computer engineering (ECE) from the Democritus University of Thrace (DUTh), Greece, in 2015, and his M.Sc. degree in ECE from DUTh, Greece, in 2017. He is currently pursuing his Ph.D. studies at University of Southampton in Electronic Materials & Devices research group, working towards the implementation of reconfigurable hybrid CMOS/memristor circuits, systems and computer architectures.
Georgios currently participates in the research of novel nano-electronic circuits and systems design for reconfigurable mixed-signal CMOS/memristor computer architectures towards developing a post-von-Neumann computing paradigm.
Jiaqi received her Bachelor of Science in Microelectronic Science and Engineering from Shenzhen University, China, in 2017. Then she obtained her Master of Science in Microelectronic Systems Design from University of Southampton, in 2018. Currently, she is pursuing her PhD in Electronic Materials & Devices Research Group, University of Southampton.
She is working towards the memristor-based analogue & mixed-signal integrated circuit (IC) design. In neural recording system, we intend to design a front-end system outputting directly a digital, back-end processed waveform corresponding to neural spikes and demonstrate extremely low power dissipation in the process. The front-end contains an integrating amplifier and a dynamic latch comparator (DLC). Instead of purely amplifying the minute signal, we opt for integrating the micron-volt level signal. The integration process not only filters out noise, but also provides amplification. With memristors implanted along the differential current path, the offset can be tuneable precisely, making the systems operate with power efficiency and high precision.
Yihan's PhD is studying on hardware development for AI at a high level design. She works on memory architectures in hyperdimensional computing designed for semantic representations.
Grahame’s research focuses on designing novel Compute-In-Memory architectures using novel devices, to address the von Neumann bottleneck. After graduating from the University of Surrey in 1984, he accumulated four decades of experience in semiconductor IC design, having held positions within Philips, Cypress, Semtech, Atmel, vivaMOS and Nordson.
Vasileios is studying the capacitive memory effects that appear in certain memristor configurations. His goal is to create variable capacitors without the need for lengthy fabrication procedures and/or moving parts.