Artificial neural networks (ANNs) are simplified models of human brain. These are networks of computing elements that have the ability to respond to input stimuli and generate the corresponding output. To obtain a desirable output, the network weights must be trained upon the available data many times. Hence the software realization of ANN takes many hours to learn a particular example. On the other hand, neural network (NN) in hardware can speed up the training by several orders of magnitude, due to the faster nature of the hardware. Different types of VLSI implementation of ANN are found in the literature. This paper provides a brief survey of digital and pulsed neurohardware. It highlights the important issues related and shows the possible direction of future research.
cited By (since 1996)1
Dr. Nirmala Devi M. and Arumugam, S., “VLSI implementation of artificial neural networks - A survey”, International Journal of Modelling and Simulation, vol. 30, pp. 148-154, 2010.