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.

中文翻译：

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胚胎硬件的智能故障预测辅助自我修复。

本文提出了新的方法，通过自我修复，故障预测和故障预测辅助的自我修复，使胚胎仿生硬件能够有效地抵御故障。所提出的自我修复功能是通过创新使用健康细胞来恢复有缺陷的胚胎细胞。通过实验，可以发现自我修复是有效的，但是硬件需要大量时间才能从突然发生的故障中恢复而无需事先警告。为了解决延迟问题，提出了基于深度学习的新颖公式用于故障预测。然后将提出的自我修复技术与提出的故障预测方法一起部署，以评估胚胎硬件的准确性和延迟。所提出的故障预测和自我修复方法已在基于FPGA的VHDL中实现。所提出的故障预测可以在较低的训练时间下实现高精度。训练时间为2.16分钟，准确性高达99.36％。提出的自我修复方法的区域开销为34％，故障恢复百分比为75％。据我们所知，这是胚胎硬件中的第一项此类工作，有望为胚胎系统的故障预测辅助自我修复开辟新的领域。