Memristive Electronics for Neuromorphic Engineering and Computing

The objective of the work is to exploit the use of memristive/resistive RAM (RRAM) devices for novel neuromorphic (neuro-inspired) computing architectures and systems. Such systems have massive potential in delivering solutions that are far superior to existing CMOS hardware in implementation of machine learning (ML) and machine intelligence. One of the main benefits is a significant reduction in circuit complexity and vast improvements in power efficiency. This is crucial for bringing ML in adaptive embedded systems. Furthermore, this could be leveraged in "Internet/Intelligence of things" era with ML algorithms implemented directly on board, facilitating efficient local data processing and enabling devices to make decisions locally, rather than to rely on data streaming and latency-prone cloud computing.
Resistive RAM (RRAM) technology, a subclass of memristive systems, is based on simple two terminal nanodevices that can repeatedly vary their resistance, with low operational energy and very high levels of integration. Remarkably, they resemble different neuronal functions - most importantly a synaptic-like plasticity by gradually changing their resistance ("synaptic weights"). By utilising the "computing-in-memory", it is possible to solve the long-lasting problem of the "von Neumann bottleneck": the need to continually shuffle data between processing cores and memory. Furthermore, implementation of hardware neural networks can be a key enabling factor for high-density, low-power neuromorphic systems.
The project will be the mix of modelling and experimental work and will include the following components/objectives:
Fabrication and structural characterisation of oxide-based neuromorphic devices;
Demonstration and optimisation of devices' intrinsic adaptive ("synaptic-like") capabilities;
Simulation and building neuromorphic circuit systems;
During the PhD, the student will gain an extensive knowledge of semiconductor processing and characterisation techniques, novel semiconductor physics, neuromorphic architectures and neuromorphic computing algorithms.

The project is relevant for the Artificial Intelligence theme within ICT.