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Abstract—The emerging neuromorphic computation provides a revo-lutionary solution to the alternative computing architecture and effec-tively extends Moore’s Law. The discovery of the memristor presents a promising hardware realization of neuromorphic systems with incredible power efficiency, allowing efficiently executing the analog matrix-vector multiplication on the memristor crossbar architecture. However, during computations, the memristor will slowly drift from its initial pro-grammed state, leading to a gradual decline of the computation precision of memristor crossbar-based computing engine (MCE). In this paper, we propose an inline calibration mechanism to guarantee the computation quality of the MCE. The inline calibration mechanism collects the MCE’s computation error through ‘interrupt-and-benchmark (IB) ’ operations and predicts the best calibration time through polynomial fitting of the computation error data. We also develop an adaptive technique to adjust the time interval between two neighbor IB operations and minimize the negative impact of the IB operation on system performance. The experiment results demonstrate that the proposed inline calibration mechanism achieves a calibration efficiency of 91.18 % on average and negligible performance overhead (i.e., 0.439%). I.
Li et al. (Mon,) studied this question.