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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.
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.
- 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.