Dr. Chris Bailey

PhD RESEARCH PROJECT SYNOPSIS

Artificial Neural Networks (ANNs) have great potential to revolutionise modern day technology. The drive toward systems with thousands of neurons has highlighted the issue of scalability and power consumption as a significant limitation as we move toward the next step, that of millions of neurons and beyond. This project therefore seeks to develop novel ideas to support highly scalable neural architectures in hardware, which have both a low-power characteristic and a high interconnect scalability. These attributes should be beneficial initially for FPGAs, but also in VLSI implementations, where transistors and wires scale at different rates.

The project will examine two key neural network approaches. The first will be traditional fixed-topology network architecture, the second will be flexible dynamically rewireable general-purpose architecture. The project will investigate both continuous valued and spiked neural networks based upon temporal-serial spike chains.

Work will include developing training schemes for both networks, building interfaces to high-level machine learning toolkits such as TensorFlow and Theano, evaluating their functionality and efficiency, and refining the hardware models before implementation on FPGA and in VLSI toolsets. Trade-offs between the two approaches and their relative suitability for FPGA vs VLSI will be examined. The core research question will be to determine if the temporal spike-chain method can achieve higher performance (according to various metrics) than the traditional approach when implemented in hardware.

The candidate would ideally have a background knowledge of VLSI tools, current knowledge of FPGA toolsets, and an interest in neural networks and their theory. Students with a strong Computer Science background and an interest in digital electronics, or an electronics student with strong mathematical/ and software skills would be well suited to the position.

The project will be supervised by Dr Chris Crispin-Bailey (Advanced Architectures Group), and Dr Suresh Manandhar (Artificial Intelligence Group).