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Understanding Sources of Inefficiency in General-Purpose Chips

机译:了解通用芯片效率低下的根源

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Due to their high volume, general-purpose processors, and now chip multiprocessors (CMPs), are much more cost effective than ASICs, but lag significantly in terms of performance and energy efficiency. This paper explores the sources of these performance and energy overheads in general-purpose processing systems by quantifying the overheads of a 720p HD H.264 encoder running on a general-purpose CMP system. It then explores methods to eliminate these overheads by transforming the CPU into a specialized system for H.264 encoding. We evaluate the gains from customizations useful to broad classes of algorithms, such as SIMD units, as well as those specific to particular computation, such as customized storage and functional units. The ASIC is 500x more energy efficient than our original four-processor CMP. Broadly, applicable optimizations improve performance by 10x and energy by 7x. However, the very low energy costs of actual core ops (100s fJ in 90nm) mean that over 90% of the energy used in these solutions is still "overhead". Achieving ASIC-like performance and efficiency requires algorithm-specific optimizations. For each sub-algorithm of H.264, we create a large, specialized functional unit that is capable of executing 100s of operations per instruction. This improves performance and energy by an additional 25x and the final customized CMP matches an ASIC solution's performance within 3x of its energy and within comparable area.
机译:由于它们的大容量,通用处理器以及现在的芯片多处理器(CMP)比ASIC具有更高的成本效益,但在性能和能效方面却大大落后。本文通过量化在通用CMP系统上运行的720p HD H.264编码器的开销,探索了通用处理系统中这些性能和能量开销的来源。然后,它探索了通过将CPU转换为H.264编码的专用系统来消除这些开销的方法。我们评估了定制的收益,这些定制对广泛的算法类别(如SIMD单元)以及特定于特定计算的定制(如定制的存储和功能单元)有用。 ASIC的能源效率比我们最初的四处理器CMP高出500倍。广泛而言,适用的优化将性能提高了10倍,能耗提高了7倍。但是,实际核心运算的能源成本非常低(在90nm时为100sfJ),这意味着这些解决方案中90%以上的能源仍是“开销”。要获得类似ASIC的性能和效率,需要进行特定于算法的优化。对于H.264的每个子算法,我们创建一个大型的专用功能单元,该功能单元每条指令可执行100多个操作。这将性能和能耗提高了25倍,最终定制的CMP在其能耗的3倍之内和可比范围内达到了ASIC解决方案的性能。

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