基于虚拟机的内存计算架构探索的定时精确仿真框架

IF 1 Q4 COMPUTER SCIENCE, INFORMATION SYSTEMS
Vincent Rietz, Christopher Münch, M. Mayahinia, M. Tahoori
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引用次数: 0

摘要

数据密集型应用对处理器与存储器之间的通信有着巨大的需求。为了减少数据传输量及其相关的延迟和能量,可以使用内存中计算(CIM)体系结构直接在内存中执行从简单的二进制操作到更复杂的操作(如加法和矩阵向量乘法)的各种操作。但是,需要对内存层次结构进行适当的调整,以支持CIM操作的执行。为了评估用于CIM的不同新兴非易失性存储器和传统计算架构之间的权衡,本工作扩展了广泛使用的gem5仿真框架,具有可扩展的时序感知主存储器CIM仿真功能。该框架用于分析具有各种新兴存储技术的CIM扩展主存储器的性能,即自旋传输扭矩磁随机存取存储器(STT-MRAM),基于氧化氧化的RAM (ReRAM)和相变存储器(PCM)。我们从PolyBench/C基准套件和其他选定的示例中评估不同的工作负载。与以处理器为中心的系统相比,结果显示大多数应用程序的执行时间显著减少。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Timing-accurate simulation framework for NVM-based compute-in-memory architecture exploration
Abstract Data-intensive applications have a huge demand on processor-memory communication. To reduce the amount of data transfers and their associated latency and energy, Compute-in-Memory (CIM) architectures can be used to perform operations ranging from simple binary operations to more complex operations such as additions and matrix-vector multiplications directly within the memory. However, proper adjustments to the memory hierarchy are needed to enable the execution of CIM operations. To evaluate the trade-off between the usage of different emerging non-volatile memories for CIM and conventional computing architectures, this work extends the widely used gem5 simulation framework with an extensible timing-aware main memory CIM simulation capability. This framework is used to analyze the performance of CIM extended main memory with various emerging memory technologies, namely Spin-Transfer-Torque Magnetic Random Access Memory (STT-MRAM), Redox-based RAM (ReRAM) and Phase-Change Memory (PCM). We evaluate different workloads from the PolyBench/C benchmark suite and other selected examples. In comparison to a processor-centric system, the results show a significant reduction in execution time for the majority of applications.
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来源期刊
IT-Information Technology
IT-Information Technology COMPUTER SCIENCE, INFORMATION SYSTEMS-
CiteScore
3.80
自引率
0.00%
发文量
29
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