NDLI: Design and VLSI implementation of an adaptive delta-sigma modulator

Content Provider	IEEE Xplore Digital Library
Author	Cauwenberghs, G.
Copyright Year	1997
Description	Author affiliation: Dept. of Electr. & Comput. Eng., Johns Hopkins Univ., Baltimore, MD, USA (Cauwenberghs, G.)
Abstract	The quality and stability of noise shaping is a concern in the design of higher-order delta-sigma modulators for high-resolution, high-speed oversampled analog-to-digital conversion. We reformulate noise-shaping modulation alternatively as a nonlinear optimal control problem, where the objective is to find the binary modulation sequence that minimizes signal swing in a cascade of integrators operating on the difference between the input signal and the modulation sequence. We use reinforcement learning to adaptively optimize a nonlinear neural classifier which outputs modulation bits from the values of the input signal and integration state variables. Analogous to the classical pole balancing control problem, a punishment signal triggers learning whenever any of the integrators saturate. We train a simple classifier consisting of locally tuned, binary address encoded neurons to produce stable noise shaping modulation, and present experimental results obtained from analog VLSI modulators of orders one and two. The integrated classifier contains an array of 64 neurons trained on-chip with a simplified variant on reinforcement learning.
Starting Page	155
Ending Page	160
File Size	680373
Page Count	6
File Format	PDF
ISBN	0818682248
ISSN	10639667
DOI	10.1109/ICVD.1998.646595
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	1998-01-04
Publisher Place	India
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Very large scale integration Delta modulation Noise shaping Learning Quantization Neurons Signal resolution Circuit noise Analog-digital conversion Optimal control
Content Type	Text
Resource Type	Article
Subject	Electrical and Electronic Engineering Hardware and Architecture

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