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A Procedure for Implanting Organized Arrays of Microwires for Single-unit Recordings in Awake, Behaving Animals

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A programmable analog VLSI neural network processor for communication receivers.

J Choi¹, S H Bang, B J Sheu

¹Dept. of Electr. Eng., Univ. of Southern California, Los Angeles, CA.

IEEE Transactions on Neural Networks

|January 1, 1993

Summary

A novel analog VLSI neural network processor enables effective communication receiver channel equalization without prior channel estimation. This chip utilizes a four-layered perceptron and a modified Kalman neuro-filtering algorithm for efficient training and performance.

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Closed-loop Neuro-robotic Experiments to Test Computational Properties of Neuronal Networks

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Published on: March 2, 2015

Area of Science:

Analog Very Large-Scale Integration (VLSI) circuits
Artificial Neural Networks (ANNs)
Communication Systems Engineering

Background:

Traditional communication receivers often require complex channel estimation techniques.
Intersymbol interference (ISI) and white Gaussian noise (WGN) degrade signal quality.
Existing equalization methods can be computationally intensive and require prior channel knowledge.

Purpose of the Study:

To design and fabricate an analog VLSI neural network processor for communication receiver applications.
To implement a powerful channel equalizer using a four-layered perceptron network.
To achieve channel equalization without the need for prior channel characteristic estimation.

Main Methods:

Fabrication of an analog VLSI neural network processor chip using 2-μm double-polysilicon CMOS technology.
Configuration of the processor as a four-layered perceptron for channel equalization.
Implementation of a modified Kalman neuro-filtering algorithm for network training on a Digital Signal Processing (DSP) board.

Main Results:

The designed VLSI chip, measuring 4.6 mm x 6.8 mm, successfully functions as a channel equalizer.
The synapse cell utilizes an enhanced wide-range Gilbert multiplier circuit for compactness.
The modified Kalman neuro-filtering algorithm demonstrated accelerated convergence for ISI and WGN channels.

Conclusions:

The analog VLSI neural network processor offers a viable solution for real-time channel equalization in communication receivers.
The proposed architecture effectively mitigates ISI and WGN without requiring pre-estimation of channel characteristics.
This approach presents a compact and efficient method for enhancing communication system performance.