Pulse Coded Neural Network Implementation In VLSI

A neural network that encodes signals in terms of pulses has been designed and fabricated. The neural network components are described in detail. As a test case, a two-layer network is implemented. A preliminary test result shows some promise and some limitations of the desig

Innovation and application of ANN in Europe demonstrated by Kohonen maps

One of the most important contributions to neural networks comes from Kohonen, Helsinki/Espoo, Finland, who had the idea of self-organizating maps in 1981. He verified his idea by an algorithm of which many applications make use of. The impetus for this idea came from biology, a field where the Europeans have always been very active at several research laboratories. The challenge was to model the self-organization found in the brain. Today one goal is the development of more sophisticated neurons which model the biological neurons more exactly. They should come to a better performance of neural nets with only a few complex neurons instead of many simple ones. A lot of application concepts arise from this idea: Kohonen himself applied it to speech recognition, but the project did not overcome much more than the recognition of the numerals one to ten at that time. A more promising application for self-organizing maps is process control and process monitoring. Several proposals were made which concern parameter classification of semiconductor technologies, design of integrated circuits, and control of chemical processes. Self-organizing maps were applied to robotics. The neural concept was introduced into electric power systems. At Dortmund we are working on a system which has to monitor the quality and the reliability of gears and electrical motors in equipment installed in coal mines. The results are promising and the probability to apply the system in the field is very high. A special feature of the system is that linguistic rules which are embedded in a fuzzy controller analyze the data of the self-organizing map in regard to life expectation of the gears. It seems that the fuzzy technique will introduce the technology of neural networks in a tandem mode. These technologies together with the genetic algorithms start to form the attractive field of computational intelligence

A digital neural network architecture using random pulse trains

A digital neural network architecture generating and processing random pulse trains, along with its unique advantages over existing comparable systems is described. In addition, test results from the VLSI implementation of its multiplication scheme are presented. These indicate that the implementation performs robustly and accurately

A digital neural network architecture using random pulse trains

A digital neural network architecture generating and processing random pulse trains, along with its unique advantages over existing comparable systems is described. In addition, test results from the VLSI implementation of its multiplication scheme are presented. These indicate that the implementation performs robustly and accurately

Pulse-firing winner-take-all networks

Winner-take-all (WTA) neural networks using pulse-firing processing elements are introduced. In the pulse-firing WTA (PWTA) networks described, input and activation signal shunting is controlled by one shared lateral inhibition signal. This organization yields an O(n) area complexity that is convenient for integrated circuit implementation. Appropriately specified network parameters allow for the accurate continuous evaluation of inputs using a signal representation compatible with established pulse-firing neural network implementations

A digital neural network architecture using random pulse trains

A digital neural network architecture generating and processing random pulse trains, along with its unique advantages over existing comparable systems is described. In addition, test results from the VLSI implementation of its multiplication scheme are presented. These indicate that the implementation performs robustly and accurately

Stochastic Digital Circuits for Probabilistic Inference

We introduce combinational stochastic logic, an abstraction that generalizes deterministic digital circuit design (based on Boolean logic gates) to the probabilistic setting. We show how this logic can be combined with techniques from contemporary digital design to generate stateless and stateful circuits for exact and approximate sampling from a range of probability distributions. We focus on Markov chain Monte Carlo algorithms for Markov random fields, using massively parallel circuits. We implement these circuits on commodity reconfigurable logic and estimate the resulting performance in time, space and price. Using our approach, these simple and general algorithms could be affordably run for thousands of iterations on models with hundreds of thousands of variables in real time