论文标题
快速可扩展的回忆内记忆分类，并使用列式算法
Fast and Scalable Memristive In-Memory Sorting with Column-Skipping Algorithm
论文作者
论文摘要
最近提出了记忆内分类，以提高硬件分类效率。使用迭代内存中的最小计算，可以消除内存和外部处理单元之间的数据运动，以提高延迟和能源效率。但是，要搜索最小值的比特 - 引言算法需要大量的列读数。在这项工作中，我们在近乎内存的电路的帮助下提出了一种柱面的算法。可以根据记录状态跳过冗余列读数，以提高延迟和硬件效率。为了增强可扩展性，我们开发了一个多银行管理，该管理可以为存储在不同的回忆存储库中的数据集提供列。原型圆柱脱落的划者是在40nm CMOS技术中使用1T1R回忆存储器实现的。在各种分类数据集上进行了实验，长度为1024 32位柱式分配程序，州记录2个分别显示了最高4.08倍的加速度，3.14倍面积效率和3.39倍的能源效率，这是最新的回忆性内存中的内存分类。
Memristive in-memory sorting has been proposed recently to improve hardware sorting efficiency. Using iterative in-memory min computations, data movements between memory and external processing units can be eliminated for improved latency and energy efficiency. However, the bit-traversal algorithm to search the min requires a large number of column reads on memristive memory. In this work, we propose a column-skipping algorithm with help of a near-memory circuit. Redundant column reads can be skipped based on recorded states for improved latency and hardware efficiency. To enhance the scalability, we develop a multi-bank management that enables column-skipping for dataset stored in different memristive memory banks. Prototype column-skipping sorters are implemented with a 1T1R memristive memory in 40nm CMOS technology. Experimented on a variety of sorting datasets, the length-1024 32-bit column-skipping sorter with state recording of 2 demonstrates up to 4.08x speedup, 3.14x area efficiency and 3.39x energy efficiency, respectively, over the latest memristive in-memory sorting.
