Artificial Neural Network Circuit for Spectral Pattern Recognition

Artificial Neural Networks (ANNs) are a massively parallel network of a large number of interconnected neurons similar to the structure of biological neurons in the human brain. ANNs find applications in a large number of fields, from pattern classification problems in Computer Science like handwriting recognition to cancer classification problems in Biomedical Engineering. The parallelism inherent in neural networks makes hardware a good choice to implement ANNs compared to software implementations. The ANNs implemented in this thesis have feedforward architecture and are trained using backpropagation learning algorithm. Different neural network models are trained offline using software and the prediction algorithms are implemented using Verilog and compared with the software models. The circuit implementation of feedforward neural networks is found to be much faster than its software counterpart because of the parallel and pipelined structure as well as the presence of a large number of computations that makes the software simulations slower in comparison. The time taken from input to output by the circuit implementing the feedforward prediction algorithm is measured from the waveform diagram, and it is seen that the circuit implementation of the ANNs provides an increase of over 90% in processing speeds obtained via post-synthesis simulation compared to the software implementation. The ANN models developed in this thesis are plant disease classification, soil clay content classification and handwriting recognition for digits. The accuracy of the ANN model is found to be 75% to 97% for the three different problems. The results obtained from the circuit implementation show a < 1% decrease in accuracy compared with the software simulations because of the use of fixed-point representation for the real numbers. Fixed-point representation of numbers is used instead of floating-point representation for faster computational speed and better resource utilization

Evolution and Competition in the Market for Handheld Computers

Since the early 1990s, electronic organisers or electronic agendas have been evolving towards fully fledged, but miniature, computers. This paper is a case study about this market. Uniquely, and reminiscent of the home computer market in the 1980s, this is a market for personal computers not dominated by Microsoft. Or at least, not yet. In tracking the evolution of this market, the paper points especially to the importance of networking and standardization. The market for handheld computers is a small market, compared to the units shipped in the market for PCs. Nevertheless a surprisingly large number of vendors has been and still is active in this market. During the short history of this market, there have been several periods where technological breakthroughs created expectations of huge growth, with entry by new suppliers as a result. As the dust settled, the losers either changed strategy, or left the market altogether. The paper will argue that standardization and networking are major factors in explaining competitive success and the recent growth of the industry.industrial organization ;

Lessons learned from the design of a mobile multimedia system in the Moby Dick project

Recent advances in wireless networking technology and the exponential development of semiconductor technology have engendered a new paradigm of computing, called personal mobile computing or ubiquitous computing. This offers a vision of the future with a much richer and more exciting set of architecture research challenges than extrapolations of the current desktop architectures. In particular, these devices will have limited battery resources, will handle diverse data types, and will operate in environments that are insecure, dynamic and which vary significantly in time and location. The research performed in the MOBY DICK project is about designing such a mobile multimedia system. This paper discusses the approach made in the MOBY DICK project to solve some of these problems, discusses its contributions, and accesses what was learned from the project

VLSI Implementation of Modified Hamming Neural Network for non Binary Pattern Recognition

Artificial intelligence is integral part of a neural network is based on mathematical equations and artificial neurons. The focus here is the implementation of the Artificial Neural Network Architecture (ANN) with on chip learning in analog VLSI for pattern recognition. It is a maximum likelohood classifier which can be implemented using VLSI. Modified Hamming neural network architecture is presented.Thenew circuit is modified to accept real time inputs as well as to determine next close pattern with respect to input pattern.Modified digit recognition circuit was simulated using HSPICE level 49 model parameters with version 3.1180n at VDD of 3V. The circuit shows power consumption of 34mW and transient delay of 0.35nS

Design and application of reconfigurable circuits and systems

Open Acces

Reconfigurable Mobile Multimedia Systems

This paper discusses reconfigurability issues in lowpower hand-held multimedia systems, with particular emphasis on energy conservation. We claim that a radical new approach has to be taken in order to fulfill the requirements - in terms of processing power and energy consumption - of future mobile applications. A reconfigurable systems-architecture in combination with a QoS driven operating system is introduced that can deal with the inherent dynamics of a mobile system. We present the preliminary results of studies we have done on reconfiguration in hand-held mobile computers: by having reconfigurable media streams, by using reconfigurable processing modules and by migrating functions