Recurrent neural networks models for analyzing single and multiple transient faults in combinational circuits. (June 2021)

• Record Type: Journal Article
• Authors: Farjaminezhad, Rasoul; Safari, S.; Moghadam, Amir Masood Eftekhari
• Is Part Of: Microelectronics journal. Volume 112(2021)
• Publication Date: 2021-06
• Subjects: Soft error -- Transient faults -- Recurrent neural networks; Microelectronics -- Periodicals; Microélectronique -- Périodiques; Microelectronics; Electronic journals; Journals - contents and abstracts; Periodicals; 621.3805
• DOI: 10.1016/j.mejo.2021.104993
• Languages: English
• ISSNs: 0959-8324

Abstract

Abstract: Transient faults analysis is an important step in circuits designing flow. By a fast and accurate scrutiny, it is possible to achieve a cost-effective and soft error tolerant system. In this paper, an efficient and accurate approach is presented to estimate the shapes of transient faults when they are propagating through the gate-level circuits. To provide a reliable prediction of how the shape of a transient fault occurring in a circuit will be, a new method based on recurrent neural networks (RNNs) is proposed. This method can make a confident estimation of the effects that the single/multiple transient faults leave while propagating through a combinational circuit. Results for a sample of 32-bit carry propagation adder shows 22x speed-up with a mean of 0.82 penalty in accuracy loss, compared to the HSPICE simulator.