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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
PubMed
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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.

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.

Related Experiment Videos

  • 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.