RNCS: Reliable Neuromorphic Computing System Design

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Deep neural networks (DNNs) are gaining increasing attention and usage in many fields related to artificial intelligence and cognitive processing. Due to challenges associated with the implementation of DNNs using traditional computing architectures, there is a growing interest for brain-inspired, aka Neuromorphic computing platforms and paradigms for direct and hence more efficient implementation of DNNs. Given the fundamental differences, in both circuitry and functionality of neuromorphic hardware realized using emerging resistive memory technologies to traditional digital circuits and architectures, there are fundamental differences in reliability requirements and methods. NCS can offer an inherent tolerance to expected errors and imperfections if their training and mapping are made explicitly aware of them. This can also be exploited to tolerate hardware failures.The purpose of this proposal is to understand and quantify the technology-relevant reliability failures on the functionality of the neuromorphic circuits and based on that, to develop various methods based on passive and active redundancy, during both training and inference phases, in order to improve the reliability of neuromorphic circuits. We also plan to improve the availability of neural networks with respect to retention and aging failures, and ensure that the system can be repaired in short amount of time to have acceptable accuracy. In addition, the system will be designed and trained such that the degradation of inference accuracy in presence of runtime failures is minimal to achieve graceful degradation.