SimAcc:用于cpu - fpga soc上定制加速器的可配置周期精确模拟器

Konstantinos Iordanou, Oscar Palomar, John Mawer, Cosmin Gorgovan, A. Nisbet, M. Luján
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引用次数: 2

摘要

本文描述了一种用于加速器IP快速计算机体系结构仿真和原型设计的灵活的基础架构。片上系统的一个趋势是在芯片上包含特定于应用程序的加速器。然而,仍然有一个关键的研究问题需要解决:硬件加速器如何与系统的处理器交互,以及对整体性能的影响是什么?为了解决这个问题,我们提出了一个基础架构,可以直接模拟未经修改的应用程序可执行程序与FPGA硬件加速器。未修改的应用程序二进制文件被动态地检测,以生成处理器加载/存储和程序计数器事件以及由加速器生成的任何内存访问,这些访问被发送到基于fpga的乱序管道模型。我们的基础设施的关键特征是能够在用户级别进行专门的编码,在运行时动态发现和使用可用的硬件模型,在异构系统中测试和同时优化硬件加速器。在评估方面,我们提出了我们的系统和Gem5之间的比较,以证明准确性和相对性能,使用SPEC CPU基准;尽管我们的系统是在Zynq XC7045上实现的,该系统集成了双667MHz Arm Cortex-A9s和大量FPGA资源,但它优于在具有32gb RAM的至强E3 3.2 GHz上运行的Gem5。我们还评估了模拟加速器与处理器交互的基础设施,使用的加速器来自Mach基准套件和其他自定义加速器来自计算机视觉应用程序。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
SimAcc: A Configurable Cycle-Accurate Simulator for Customized Accelerators on CPU-FPGAs SoCs
This paper describes a flexible infrastructure for fast computer architecture simulation and prototyping of accelerator IP. A trend for System-on-Chips is to include application specific accelerators on the die. However, there is still a key research problem that needs to be addressed: How do hardware accelerators interact with the processors of a system and what is the impact on overall performance? To solve this problem, we propose an infrastructure that can directly simulate unmodified application executables with FPGA hardware accelerators. Unmodified application binaries are dynamically instrumented to generate processor load/store and program counter events and any memory accesses generated by accelerators, that are sent to an FPGA-based out-of-order pipeline model. The key features of our infrastructure are the ability to code exclusively at the user level, to dynamically discover and use available hardware models at run time, to test and simultaneously optimize hardware accelerators in an heterogeneous system. In terms of evaluation, we present a comparison between our system and Gem5 to demonstrate accuracy and relative performance, using the SPEC CPU benchmarks; even though our system is implemented on Zynq XC7045 which integrates dual 667MHz Arm Cortex-A9s with substantial FPGA resources, it outperforms Gem5 running on a Xeon E3 3.2 GHz with 32GBs of RAM. We also evaluate our infrastructure in simulating the interaction of accelerators with processors using accelerators taken from the Mach Benchmark Suite and other custom accelerators from computer vision applications.
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