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A supervised neural network layer of continuously adapting, analog floating-gate nodes

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3 Author(s)
Dugger, J. ; Georgia Inst. of Technol., Atlanta, GA, USA ; Srinivasan, V. ; Hasler, P.

We present an LMS node based upon an improved continuously adapting, analog floating-gate synapse that exhibits minimal weight decay. Our approach is based upon a recently developed synapse element based upon our tradition on single-transistor learning synapses with minimal weight decay. We show the transition from a single floating-gate synapse element to a single floating-gate node, demonstrated using results from simple LMS experiments. We present experimental data from ICs fabricated in 0.5 μm CMOS process.

Published in:

Signals, Systems and Computers, 2004. Conference Record of the Thirty-Seventh Asilomar Conference on  (Volume:2 )

Date of Conference:

9-12 Nov. 2003