Publication Type:

Journal Article

Source:

International Journal of Modelling and Simulation, Taylor & Francis, Volume 30, Number 2, p.148-154 (2010)

URL:

http://www.scopus.com/inward/record.url?eid=2-s2.0-77957041673&partnerID=40&md5=b60ccb8f14192f9690ca73ffe4114d02

Keywords:

Artificial Neural Network, Artificial neural networks, Brain models, Computing element, Desirable outputs, Digital neurohardware, Human brain, Network weights, Neural networks, Orders of magnitude, Pulsed neurohardware, Simplified models, Speed-ups, Surveys, VLSI implementation

Abstract:

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.

Notes:

cited By (since 1996)1

Cite this Research Publication

M. Nirmala Devi and Arumugam, S., “VLSI implementation of artificial neural networks - A survey”, International Journal of Modelling and Simulation, vol. 30, pp. 148-154, 2010.