Memristive technology has been widely explored, due to its distinctive properties, such as nonvolatility, high density,versatility, and CMOS compatibility. For memristive devices, a general compact model is highly favorable for the realization of its circuits and applications. In this paper, we propose a novel memristive model of TiO_x-based devices, which considers the negative differential resistance(NDR) behavior. This model is physics-oriented and passes Linn's criteria. It not only exhibits sufficient accuracy(IV characteristics within 1.5% RMS), lower latency(below half the VTEAM model),and preferable generality compared to previous models, but also yields more precise predictions of long-term potentiation/depression(LTP/LTD). Finally, novel methods based on memristive models are proposed for gray sketching and edge detection applications. These methods avoid complex nonlinear functions required by their original counterparts. When the proposed model is utilized in these methods, they achieve increased contrast ratio and accuracy(for gray sketching and edge detection, respectively) compared to the Simmons model. Our results suggest a memristor-based network is a promising candidate to tackle the existing inefficiencies in traditional image processing methods.
随着超级计算机规模迅速增大,可靠性成为制约系统可用性的主要问题。现有容错机制,包括检查点技术和进程冗余等,不能有效解决该问题。为此,提出一种基于进程复制和预取的高性能计算容错框架—FTRP(fault tolerance framework using process replication and prefetching),该框架兼具主动和被动容错机制的优点,引入创新的开销模型和主动容错机制,能够有效改善应用运行效率。提出"工作最多"(work-most,WM)的创新开销模型,基于故障预测结果和应用状态,从容错机制集中在线自适应给出运行容错决策。与程序运行过程中的局部性相似,我们第一次观察到超级计算机故障局部性现象。基于故障局部性,提出一种新的进程复制和进程预取相结合的容错机制,无论故障能否被预测到,都能够有效避免故障引起的损失。通过基于实际故障路径和普通故障预测准确率的模拟实验,并采用FTRP容错框架的应用,可以获得比现有容错机制10%的改进,且在P级甚至更大规模系统上有效。
