Intelligent Fault-Prediction Assisted Self-Healing for Embryonic Hardware

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.