Beta-CMOS implementation of an artificial neuron

Victor I. Varshavsky; Vyacheslav B. Marakhovsky

doi:10.1117/12.342874

22 March 1999 Beta-CMOS implementation of an artificial neuron

Victor I. Varshavsky, Vyacheslav B. Marakhovsky

Proceedings Volume 3722, Applications and Science of Computational Intelligence II; (1999) https://doi.org/10.1117/12.342874
Event: AeroSense '99, 1999, Orlando, FL, United States

Abstract

The improved version of digital-analog CMOS implementation of an artificial neuron is discussed. This neuron is learnable to logical threshold functions, being functionally powerful and highly noise-stable. It is built on the basis of a previously suggested circuit consisting of synapses, (beta) -comparator and output amplifier. Every learnable synapse contains 5 minimum transistors and a capacitor for storing the results of the learning. It has been shown that higher non-linearity of the (beta) -comparator in the threshold zone can sharply increase the threshold of the realized functions and noise- stability of the neuron. To increase the minimum leap of voltage at the (beta) -comparator output in the threshold zone which is attainable during the teaching, it is suggested to use an output amplifier with threshold hysteresis. For this aim, the neuron has three output amplifiers with different thresholds. The output of the amplifier with the middle value of threshold is the output of the neuron; the outputs of the other two amplifiers are used during the teaching. The way is suggested of refreshing the voltages (found during the teaching) on the capacitors during the evaluation process. The results of SPICE simulation prove that the neuron is learnable to most complicated threshold functions of 10 and more variables and that it is capable to maintain the learned state for a long time. In the simulation, transistor modes MOSIS BSIM3v3.1 0.8 micrometer were used.

Citation Download Citation

Victor I. Varshavsky and Vyacheslav B. Marakhovsky "Beta-CMOS implementation of an artificial neuron", Proc. SPIE 3722, Applications and Science of Computational Intelligence II, (22 March 1999); https://doi.org/10.1117/12.342874

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available