论文标题
AIDX：自适应推理方案，以减轻Memristive VMM加速器中的状态饮用
AIDX: Adaptive Inference Scheme to Mitigate State-Drift in Memristive VMM Accelerators
论文作者
论文摘要
基于调整输入电压脉冲的持续时间和振幅的优化方案，提出了一种横杆（AIDX）的自适应推理方法。 AIDX最大程度地减少了孕再次漂移对人工神经网络准确性的长期影响。 Memristor的子阈值行为已通过与制造的设备数据进行建模和验证。该方法已通过测试不同的网络结构和应用程序，例如图像重建和分类任务来评估。结果表明，在10000推理操作后，CIFAR10数据集的卷积神经网络（CNN）的平均提高了60％，图像重建的误差降低了78.6％。
An adaptive inference method for crossbar (AIDX) is presented based on an optimization scheme for adjusting the duration and amplitude of input voltage pulses. AIDX minimizes the long-term effects of memristance drift on artificial neural network accuracy. The sub-threshold behavior of memristor has been modeled and verified by comparing with fabricated device data. The proposed method has been evaluated by testing on different network structures and applications, e.g., image reconstruction and classification tasks. The results showed an average of 60% improvement in convolutional neural network (CNN) performance on CIFAR10 dataset after 10000 inference operations as well as 78.6% error reduction in image reconstruction.
