Approximate computing (AC) leverages the inherent error resilience and is used in many big-data applications from various domains such as multimedia, computer vision, signal processing, and machine learning to improve systems performance and power consumption. Like many other approximate circuits and algorithms, the memory subsystem can also be used to enhance performance and save power significantly. This paper proposes an efficient and effective systematic methodology to construct an approximate non-volatile magneto-resistive RAM (MRAM) framework using consumer-off-the-shelf (COTS) MRAM chips. In the proposed scheme, an extensive experimental characterization of memory errors is performed by manipulating the write latency of MRAM chips which exploits the inherent (intrinsic/extrinsic process variation) stochastic switching behavior of magnetic tunnel junctions (MTJs). The experimental results and error-resilient image application reveal that the proposed AC framework provides a significant performance improvement and demonstrates a maximum reduction in MRAM write current of ~66% on average with negligible or no loss in output quality.

中文翻译：

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近似 MRAM：使用 MRAM 芯片进行高性能和高能效计算，用于容错应用

近似计算 (AC) 利用固有的错误恢复能力，并用于多媒体、计算机视觉、信号处理和机器学习等各个领域的许多大数据应用程序，以提高系统性能和功耗。与许多其他近似电路和算法一样，内存子系统也可用于显着提高性能和节省功耗。本文提出了一种高效且有效的系统方法，以使用消费者现货供应 (COTS) MRAM 芯片构建近似的非易失性磁阻 RAM (MRAM) 框架。在提议的方案中，通过操纵 MRAM 芯片的写入延迟，对存储器错误进行了广泛的实验表征，该延迟利用磁隧道结 (MTJ) 的固有（内在/外在工艺变化）随机切换行为。实验结果和容错图像应用表明，所提出的 AC 框架提供了显着的性能改进，并证明了 MRAM 写入电流的最大降低平均约 66%，输出质量的损失可以忽略不计或没有损失。