107 research outputs found
Analogue neuromorphic systems.
This thesis addresses a new area of science and technology, that of neuromorphic
systems, namely the problems and prospects of analogue neuromorphic systems. The
subject is subdivided into three chapters.
Chapter 1 is an introduction. It formulates the oncoming problem of the creation
of highly computationally costly systems of nonlinear information processing (such as
artificial neural networks and artificial intelligence systems). It shows that an analogue
technology could make a vital contribution to the creation such systems. The basic principles
of creation of analogue neuromorphic systems are formulated. The importance
will be emphasised of the principle of orthogonality for future highly efficient complex
information processing systems.
Chapter 2 reviews the basics of neural and neuromorphic systems and informs on
the present situation in this field of research, including both experimental and theoretical
knowledge gained up-to-date. The chapter provides the necessary background for
correct interpretation of the results reported in Chapter 3 and for a realistic decision on
the direction for future work.
Chapter 3 describes my own experimental and computational results within the
framework of the subject, obtained at De Montfort University. These include: the
building of (i) Analogue Polynomial Approximator/lnterpolatoriExtrapolator, (ii) Synthesiser
of orthogonal functions, (iii) analogue real-time video filter (performing the
homomorphic filtration), (iv) Adaptive polynomial compensator of geometrical distortions
of CRT- monitors, (v) analogue parallel-learning neural network (backpropagation
algorithm).
Thus, this thesis makes a dual contribution to the chosen field: it summarises the
present knowledge on the possibility of utilising analogue technology in up-to-date and
future computational systems, and it reports new results within the framework of the
subject. The main conclusion is that due to its promising power characteristics, small
sizes and high tolerance to degradation, the analogue neuromorphic systems will playa
more and more important role in future computational systems (in particular in systems
of artificial intelligence)
Analog and Neuromorphic computing with a framework on a reconfigurable platform
The objective of the research is to demonstrate energy-efficient computing on a configurable platform, the Field Programmable Analog Array (FPAA), by leveraging analog strengths, along with a framework, to enable real-time systems on hardware. By taking inspiration from biology, fundamental blocks of neurons and synapses are built, understanding the computational advantages of such neural structures. To enable this computation and scale up from these modules, it is important to have an infrastructure that adapts by taking care of non-ideal effects like mismatches and variations, which commonly plague analog implementations. Programmability, through the presence of floating gates, helps to reduce these variations, thereby ultimately paving the path to take physical approaches to build larger systems in a holistic manner.Ph.D
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