An instruction systolic array architecture for multiple neural network types

Modern electronic systems, especially sensor and imaging systems, are beginning to incorporate their own neural network subsystems. In order for these neural systems to learn in real-time they must be implemented using VLSI technology, with as much of the learning processes incorporated on-chip as is possible. The majority of current VLSI implementations literally implement a series of neural processing cells, which can be connected together in an arbitrary fashion. Many do not perform the entire neural learning process on-chip, instead relying on other external systems to carry out part of the computation requirements of the algorithm. The work presented here utilises two dimensional instruction systolic arrays in an attempt to define a general neural architecture which is closer to the biological basis of neural networks - it is the synapses themselves, rather than the neurons, that have dedicated processing units. A unified architecture is described which can be programmed at the microcode level in order to facilitate the processing of multiple neural network types. An essential part of neural network processing is the neuron activation function, which can range from a sequential algorithm to a discrete mathematical expression. The architecture presented can easily carry out the sequential functions, and introduces a fast method of mathematical approximation for the more complex functions. This can be evaluated on-chip, thus implementing the entire neural process within a single system. VHDL circuit descriptions for the chip have been generated, and the systolic processing algorithms and associated microcode instruction set for three different neural paradigms have been designed. A software simulator of the architecture has been written, giving results for several common applications in the field

High-resolution sonar DF system

One of the fundamental problems of sonar systems is the determination of the bearings of underwater sources/targets. The classical solution to this problem, the 'Conventional Beamformer', uses the outputs from the individual sensors of an acoustic array to form a beam which is swept across the search sector. The resolution of this method is limited by the beam width and narrowing this beam to enhance the resolution may have some practical problems, especially in low frequency sonar, because of the physical size of the array needed. During the past two decades an enormous amount of work has been done to develop new algorithms for resolution enhancements beyond that of the Conventional Beamformer. However, most of these methods have been based on computer simulations and very little has been published on the practical implementation of these algorithms. One of the main reasons for this has been the lack of hardware that can handle the relatively heavy computational load of these algorithms. However, there have been great advances in semiconductor and computer technologies in the last few years which have led to the availability of more powerful computational and storage devices. These devices have opened the door to the possibility of implementing these high-resolution Direction Finding (DF) algorithms in real sonar systems. The work presented in this thesis describes a practical implementation of some of the high-resolution DF algorithms in a simple sonar system that has been designed and built for this purpose. [Continues.

Personalised Finite-Element Models using Image Registration in Parametric Space

Heart failure (HF) is a chronic clinical condition in which the heart fails to pump enough blood to meet the metabolic needs of the body. Patients have reduced physical performance and can see their quality of life severely impaired; around 40-70% of patients diagnosed of HF die within the first year following diagnosis. It is underestimated that 900,000 people in the UK currently suffer from HF. HF has a big impact on the NHS, representing 1 million inpatient bed, 5% of all emergency medical admission to hospitals and costs 2% of the total NHS budget. The annual incidence of new diagnoses is reported as 93,000 people in England alone – and this figure is already increasing at a rate above that at which population is ageing [1]. Cardiac resynchronisation therapy (CRT) has become established as an effective solution to treat selected patients with HF. The research presented in this thesis has been conducted as part of a large EPSRC-Funded project on the theme of Grand Challenges in Heathcare, with co-investigators from King’s College London (KCL), Imperial College London, University College London (UCL) and the University of Sheffield. The aim is to develop and to apply modelling techniques to simulate ventricular mechanics and CRT therapy in patient cohorts from Guy’s Hospital (London) and from the Sheffield Teaching Hospitals Trust. This will lead to improved understanding of cardiac physiological behaviour and how diseases affect normal cardiac performance, and to improved therapy planning by allowing candidate interventions to be simulated before they are applied on patients. The clinical workflow within the hospital manages the patient through the processes of diagnosis, therapy planning and follow-up. The first part of this thesis focuses on the development of a formal process for the integration of a computational analysis workflow, including medical imaging, segmentation, model construction, model execution and analysis, into the clinical workflow. During the early stages of the project, as the analysis workflow was being compiled, a major bottle-neck was identified regarding the time required to build accurate, patient-specific geometrical meshes from the segmented images. The second part of this thesis focuses on the development of a novel approach based on the use of image registration to improve the process of construction of a high-quality personalised finite element mesh for an individual patient. Chapter 1 summarises the clinical context and introduces the tools and processes that are applied in this thesis. Chapter 2 describes the challenges and the implementation of a computational analysis workflow and its integration into a clinical environment. Chapter 3 describes the theoretical underpinnings of the image registration algorithm that has been developed to address the problem of construction of high-quality personalised meshes. The approach includes the use of regularisation terms that are designed to improve the mesh quality. The selection and implementation of the regularisation terms is discussed in detail in Chapter 4. Chapter 5 describes the application of the method to a series of test problems, whilst Chapter 6 describes the application to the patient cohort in the clinical study. Chapter 7 demonstrates that the method, developed for robust mesh construction, can readily be applied to determine boundary conditions for computational fluid dynamics (CFD) analysis. Chapter 8 provides a summary of the achievements of the thesis, together with suggestions for further work

Improved matrix triangularisation using a double pipeline systolic array

A new systolic array for triangularisation with reduced computation time and latency is described. The array requires T=5n/2+m/2 steps for an n×m matrix where m > n and is laid out on two layers with communication between layers occuring only at the edge of the design