MRAM中一种快速有效的最小/最大搜索方法

Amitesh Sridharan, Fan Zhang, Deliang Fan
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引用次数: 1

摘要

内存计算(IMC)技术被认为是解决数据密集型应用中众所周知的内存墙难题的一种很有前途的方法。在本文中,我们首先提出了MnM,这是一种新颖的IMC系统,具有创新的架构/电路设计,可以在新兴的自旋轨道扭矩磁随机存取存储器(SOT-MRAM)中快速高效地进行最小/最大搜索计算。我们提出的基于SOT-MRAM的内存逻辑电路经过专门优化,可以执行并行的单周期XNOR逻辑,这些逻辑在最小/最大内存搜索算法中大量使用。与大多数先前使用多感测放大器或复杂CMOS逻辑门的方法相比,我们的新型内存XNOR电路每行只有两个晶体管的开销。我们还设计了所有其他所需的外围电路,以实现完整的mram最小/最大搜索计算。我们对Dijkstra算法和其他排序算法在真实单词数据集上的跨层综合实验表明,我们的MnM可以在cpu, gpu和其他基于RRAM/MRAM/DRAM的竞争IMC平台上取得显着的性能提升。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
MnM: A Fast and Efficient Min/Max Searching in MRAM
In-Memory Computing (IMC) technology has been considered to be a promising approach to solve well-known memory-wall challenge for data intensive applications. In this paper, we are the first to propose MnM, a novel IMC system with innovative architecture/circuit designs for fast and efficient Min/Max searching computation in emerging Spin-Orbit Torque Magnetic Random Access Memory (SOT-MRAM). Our proposed SOT-MRAM based in-memory logic circuits are specially optimized to perform parallel, one-cycle XNOR logic that are heavily used in the Min/Max searching-in-memory algorithm. Our novel in-memory XNOR circuit also has an overhead of just two transistors per row when compared to most prior methodologies which typically use multiple sense amplifiers or complex CMOS logic gates. We also design all other required peripheral circuits for implementing complete Min/Max searching-in-MRAM computation. Our cross-layer comprehensive experiments on Dijkstra's algorithm and other sorting algorithms in real word datasets show that our MnM could achieve significant performance improvement over CPUs, GPUs, and other competing IMC platforms based on RRAM/MRAM/DRAM.
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