The authors present a design method of neural networks for optimization problems, which can tolerate the simultaneous existence of both stuck-at-zero and ...
In this paper we present a design method of neural networks for optimization problems, which can toler- ate the simultaneous existence of both stuck-at-0 ...
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Yoshihiro Tohma, Yoichi Koyanagi: Design of Neural Networks to Tolerate the Mixture of Two Types of Faults. FTCS 1993: 268-277. manage site settings.
Y. Koyanagi's research works | Fujitsu Ltd. and other places
www.researchgate.net › Y-Koyanagi-610...
Y. Koyanagi's 3 research works with 28 citations, including: Fault-tolerant design of neural networks for solving optimization problems.
This research focuses on designing an individual and multiple fault detection and isolation system based on artificial neural networks for an internal ...
Missing: Tolerate | Show results with:Tolerate
Design of neural networks to tolerate the mixture of two types of faults. Proc 23rd Int Symp on Fault-Tolerant Computing (FTCS-23), Toulouse, 1993; p 268–278.
The inherent fault tolerance of Neural Networks can be improved with regularization, however, the current techniques ex- hibit a trade-off between ...
Performance evaluation measures were developed and used to quantify network tolerance to faults such as single link failures, multiple node failures, multiple ...
Missing: Mixture | Show results with:Mixture
Extensive experimental studies on different DNN models and datasets confirm that our design significantly reduces the neural competition and increases the ...
Missing: Mixture | Show results with:Mixture
Jun 14, 2024 · Our study demonstrates that the RNS-based approach can achieve ≥99% FP32 accuracy with 6-bit integer arithmetic for DNN inference and 7-bit for DNN training.