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A note on the complexity of reliability in neural networks
P Berman1, I Parberry, G Schnitger
1Dept. of Comput. Sci., Pennsylvania State Univ., University Park, PA.
IEEE Transactions on Neural Networks
|January 1, 1992
Summary
Achieving fault tolerance in discrete neural networks requires a log-linear increase in neurons and constant time overhead. This holds even with arbitrary fan-in, offering robust computational models.
Area of Science:
- Computer Science
- Artificial Intelligence
- Neural Networks
Background:
- Discrete neural network models are susceptible to random malicious faults.
- Ensuring computational reliability is crucial for practical applications.
Purpose of the Study:
- To investigate methods for achieving fault tolerance in discrete neural network models.
- To quantify the resource overhead associated with fault tolerance.
Main Methods:
- Analysis of a standard discrete neural network model with small fan-in.
- Consideration of a nonstandard model with unrestricted fan-in.
- Evaluation of resource requirements (neurons, parallel time) for fault tolerance.
Main Results:
- Fault tolerance is achievable with a log-linear increase in neurons and constant factor increase in parallel time for standard models.
- Similar fault tolerance is achieved in nonstandard models with no fan-in restriction.
- Arbitrary fan-in is a key condition for efficient fault tolerance.
Conclusions:
- The study demonstrates a feasible approach to enhance the robustness of discrete neural networks against faults.
- The findings suggest that fault tolerance can be implemented with manageable increases in computational resources.
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