期刊
IEEE TRANSACTIONS ON BIOMEDICAL CIRCUITS AND SYSTEMS
卷 14, 期 4, 页码 852-866出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TBCAS.2020.2995784
关键词
Circuit faults; Hardware; Delays; Principal component analysis; Transient analysis; Prediction methods; Machine learning; Bio-inspired hardware; ELSTM; Fault Pre-diction; Machine Learning; Neural Network; PCA; RPCA; Self-Healing
This paper proposes novel methods for making embryonic bio-inspired hardware efficient against faults through self-healing, fault prediction, and fault-prediction assisted self-healing. The proposed self-healing recovers a faulty embryonic cell through innovative usage of healthy cells. Through experimentations, it is observed that self-healing is effective, but it takes a considerable amount of time for the hardware to recover from a fault that occurs suddenly without forewarning. To get over this problem of delay, novel deep learning-based formulations are proposed for fault predictions. The proposed self-healing technique is then deployed along with the proposed fault prediction methods to gauge the accuracy and delay of embryonic hardware. The proposed fault prediction and self-healing methods have been implemented in VHDL over FPGA. The proposed fault predictions achieve high accuracy with low training time. The accuracy is up to 99.36% with the training time of 2.16 min. The area overhead of the proposed self-healing method is 34%, and the fault recovery percentage is 75%. To the best of our knowledge, this is the first such work in embryonic hardware, and it is expected to open a new frontier in fault-prediction assisted self-healing for embryonic systems.
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