Mixed-Mode Artificial Neuron for CMOS Integration

A new approach to designing artificial neurons in CMOS technology is proposed in this paper. Design and simulation results of the basic building blocks are presented. Programmable weights are obtained using a current-mode mixed-signal four-quadrant multiplier, whereas the non-linear output function...

Full description

Saved in:
Bibliographic Details
Main Authors: Zatorre, G., Medrano, N., Sanz, M.T., Martinez, P.A., Celma, S.
Format: Conference Proceeding
Language:English
Subjects:
Analog-digital conversion
Artificial neural networks
Circuit simulation
CMOS technology
Energy consumption
Inverters
Magnetic sensors
Multilayer perceptrons
Neurons
Voltage
Online Access:Request full text
Tags: Add Tag
No Tags, Be the first to tag this record!
Description
Summary:A new approach to designing artificial neurons in CMOS technology is proposed in this paper. Design and simulation results of the basic building blocks are presented. Programmable weights are obtained using a current-mode mixed-signal four-quadrant multiplier, whereas the non-linear output function is implemented with a specifically designed class AB current conveyor. Starting from circuit simulation results, the behaviour of the proposed neuron was modeled. A multilayer perceptron network implemented with the new artificial neuron structure was trained to tackle linearization of a giant magneto-resistive sensor. Simulation results show the efficiency of the new implementation
ISSN:2158-8473
2158-8481
DOI:10.1109/MELCON.2006.1653118