Power efficient deep neural networks using analogue and neuromorphic circuits for biomedical applications

Deep neural networks (DNNs) typically consist of many layers of neurons coupled among each other by weighted routing. DNNs have recently exhibited state-of-art performance in different applications. For example, convolutional neural network (CNN) is widely applied to image processing, and recurrent neural network (RNN) is used for natural language processing. However, most of these applications are performed on convolutional computing systems, which are ideally unsuited for implementing such massively parallel architectures. Neuromorphic computing systems show a new non-von Neumann massively parallel architecture that is ideally suited to implementing DNNs. Neuromorphic circuits that emulate neural dynamics, like the brain, are more suitable for signal processing of visual, auditory, olfactory and other sensory systems. This real-time and energy-efficient design also matches the requirement of medical applications. This project will seek inspiration from the most cutting-edge cognitive and perceptual mechanisms in brain science and neuroscience and try to establish a learning algorithm for analogue CMOS circuits. The circuit design of the neurons needs to realize the bi-direction transmission of the signal in order to transplant the existing training algorithm from the digital system to the analogue system. In the future, CMOS-compatible memristive devices will be introduced to achieve co-localization of computation and memory.