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Fast Instruction Cache Modeling for Approximate Timed HW/SW Co-Simulation

机译:近似定时HW / SW共模的快速指令缓存建模

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Approximate timed co-simulation has been proposed as a fast solution for system modeling in early design steps. This co-simulation technique enables the simulation of systems at speeds close to functional execution, while considering timing effects. As a consequence, system performance estimations can be obtained early, enabling efficient design space exploration and system refinement. To achieve fast simulation speeds, first the SW code is pre-annotated with time information and then it is natively executed, performing what is called native-based co-simulation. To obtain sufficiently accurate performance estimations, the effect of the system components must be considered. Among them, processor caches are really important, as they have a strong impact on the overall system performance. However, no efficient techniques for cache modeling in native-based co-simulation have been proposed. Previous works considering caches apply slow cache models based on tag search, similar to ISS-based models. This solution slows down the simulation speed, greatly reducing the efficiency of native based co-simulations. In this paper, a high-level instruction cache model is proposed, along with the required instrumentation for native simulation. This model allows the designer to obtain cache hit/miss rate estimations with simulation speeds very close to native execution. Results present a speed-up of two orders of magnitude with respect to ISS and one order of magnitude regarding previous approaches in native simulation. Miss rate estimation error remains below 5%.
机译:已经提出了近似定时共模作为早期设计步骤中系统建模的快速解决方案。该协同仿真技术可以在考虑定时效果的同时以接近功能执行的速度模拟系统。因此,可以提前获得系统性能估计,从而实现有效的设计空间探索和系统改进。为了实现快速的模拟速度,首先使用时间信息预注释SW代码,然后本地执行它,执行所谓的基于本机的共模。为了获得足够精确的性能估计,必须考虑系统组件的效果。其中,处理器缓存非常重要,因为它们对整体系统性能产生了强烈影响。然而,已经提出了基于本机的共模中的高速缓存建模的有效技术。以前的作品考虑缓存基于标签搜索应用慢速缓存模型,类似于基于ISS的模型。该解决方案减慢了模拟速度,大大降低了基于天然的共模的效率。本文提出了一种高级指令高速缓存模型,以及本机仿真所需的仪器。该模型允许设计人员使用非常接近本机执行的仿真速度获得缓存命中/错过速率估计。结果对ISS和一个数量级的速度提高了两个数量级,关于先前的天然模拟方法。错过率估计误差仍然低于5%。

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