CortexSuite: A synthetic brain benchmark suite
CortexSuite: A synthetic brain benchmark suite
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CortexSuite：合成大脑基准测试套件
DOI：
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发表时间：
2014
期刊：
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通讯作者：
M. Taylor
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作者：
Shelby Thomas;Chetan Gohkale;Enrico Tanuwidjaja;Tony Chong;David Lau;Saturnino Garcia;M. Taylor
如今，许多传统的终端用户应用程序都被认为在现有机器上“运行得足够快”，因此，对能够利用我们不断发展的硬件的新功能的新颖应用程序的搜索仍在继续。在这些潜在的应用中，最重要的是那些围绕信息处理能力聚集的应用，这些能力是人类今天拥有的，但计算机所缺乏的。大脑可以进行这些计算的事实证明了这些应用是可以实现的。与此同时，我们经常发现，人类的神经系统，拥有800亿个神经元，在某些指标上，比今天的机器更强大，更节能。这两个方面都使这类应用程序成为架构基准套件的理想目标，因为有证据表明这些应用程序既有用又具有计算挑战性。本文详细介绍了CortexSuite，一个合成大脑基准套件，它试图捕获这种工作量。我们通过类比人类神经处理功能对CortexSuite中的基准进行分类和识别。我们使用大脑皮层的主要叶作为数据处理算法的组织和分类模型。需要明确的是，我们的目标不是在神经元层面上模拟大脑，而是将具有相似功能并在现实世界中取得成功的人工合成算法收集在一起。我们咨询了六位世界级的机器学习和计算机视觉研究人员，他们在各自不同的子领域共有83,091次引用，我们要求他们确定将在未来十年产生重大影响的新兴计算密集型算法或应用程序。这与反映实际使用算法哲学的数据集相结合，并以“干净的C”编码，以便并行和近似编译器和架构研究人员可以访问，分析和使用它们。
These days, many traditional end-user applications are said to “run fast enough” on existing machines, so the search continues for novel applications that can leverage the new capabilities of our evolving hardware. Foremost of these potential applications are those that are clustered around information processing capabilities that humans have today but are lacking in computers. The fact that brains can perform these computations serves as an existence proof that these applications are realizable. At the same time, we often discover that the human nervous system, with its 80 billion neurons, on some metrics, is more powerful and energy-efficient than today's machines. Both of these aspects make this class of applications a desirable target for an architectural benchmark suite, because there is evidence that these applications are both useful and computationally challenging. This paper details CortexSuite, a Synthetic Brain Benchmark Suite, which seeks to capture this workload. We classify and identify benchmarks within CortexSuite by analogy to the human neural processing function. We use the major lobes of the cerebral cortex as a model for the organization and classification of data processing algorithms. To be clear, our goal is not to emulate the brain at the level of the neuron, but rather to collect together synthetic, man-made algorithms that have similar function and have met with success in the real world. We consulted six world-class machine learning and computer vision researchers, who collectively hold 83,091 citations across their distinct subareas, asking them to identify newly emerging computationally-intensive algorithms or applications that are going to have a large impact over the next ten years. This is coupled with datasets that reflect the philosophy of practical use algorithms and are coded in “clean C” so as to make them accessible, analyzable, and usable for parallel and approximate compiler and architecture researchers alike.