ICE: Inline calibration for memristor crossbar-based computing engine

The emerging neuromorphic computation provides a revolutionary solution to the alternative computing architecture and effectively 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 programmed 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 (I&B)´ 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 I&B operations and minimize the negative impact of the I&B 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%).