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Implementation issues of neuro-fuzzy hardware: going toward HW/SW codesign.

¹Dipt. di Elettronica, Politecnico di Torino, Italy.

IEEE Transactions on Neural Networks

|February 2, 2008

Summary

This study overviews hardware implementations for artificial neural and fuzzy systems. Hardware/software codesign offers the most promising approach for efficient neuro-fuzzy system development.

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Area of Science:

Computer Engineering
Artificial Intelligence

Background:

Existing hardware implementations for artificial neural and fuzzy systems are reviewed.
Limitations, advantages, and drawbacks of analog, digital, and pulse stream techniques are discussed.

Purpose of the Study:

To analyze hardware performance parameters, tradeoffs, and bottlenecks in various implementation methodologies.
To describe hardware technology constraints on algorithms and performance.

Main Methods:

Annotated overview of current hardware implementation techniques.
Analysis of performance parameters, tradeoffs, and bottlenecks.
Investigation of hardware technology constraints.

Main Results:

Identified limitations and advantages of different hardware implementation techniques.

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Highlighted intrinsic bottlenecks in several methodologies.

Described hardware technology constraints impacting algorithms and performance.

Conclusions:

Hardware/software codesign is the most promising research area for neuro-fuzzy systems.
This approach enables fast design of complex systems with optimal performance/cost ratio.