摘要
由一名新闻记者兼机器人与机器学习每日新闻的新闻编辑-一项关于人工智能的新研究现在可用。根据NewsRx记者来自海湾科技大学的新闻，研究表明，"为了更好地设计人工智能处理器，关键是要表征人工智能(AI)工作负载，并将其与普通个人计算机(PC)工作负载进行对比。"新闻记者从海湾科技大学的研究中获得了一句话：“在这项工作中，我们在多核计算机上使用Intel oneAPI VTune Profiler分析了AIBench和PassMark符合共振峰测试基准。我们捕获并对比了这两个不同基准的各种CPU和平台指标以及事件计数。使用Orange 3.0数据挖掘工具，然后,根据捕获的轮廓度量和事件计数,训练和测试了9个机器学习(ML)模型,对这两个基准测试的CPI和经过时间进行分类,包括在AIBench中推理和训练TE sts,以及CPU、内存、图形、linea r回归机器学习模型是最佳的每指令时钟(CPI)分类器，这种机器学习分类方法可以帮助预测CPI和经过时间，并根据所描述的应用程序(Profiled application)和捕获的概要度量和事件计数区分AI和标准PC Workl OAD。
Abstract
By a News Reporter-Staff News Editor at Robotics & Machine Learning Daily News Daily News – A new study on artificial intelligence is now available. According to news originating from Gulf University for Scienc e and Technology by NewsRx correspondents, research stated, “To better design AI processors, it is critical to characterize artificial intelligence (AI) workloa ds and contrast them to normal personal computer (PC) workloads.” The news journalists obtained a quote from the research from Gulf University for Science and Technology: “In this work, we profiled the AIBench and PassMark Per formanceTest benchmarks with the Intel oneAPI VTune Profiler on a multi-core com puter. We captured and contrasted the various CPU and platform metrics and event counts for these two distinct benchmarks. Using the Orange 3.0 data mining tool , and based on the captured profile metrics and event counts, we then trained an d tested 9 machine learning (ML) models to classify the CPIs and elapsed times o f the various tests of these two benchmarks, including inference and training te sts in AIBench, and CPU, memory, graphics, and disk tests in PassMark. The linea r regression machine learning model emerged as the best clocks per instruction ( CPI) classifier, while the neural network model with 4 hidden layers was the bes t elapsed time classifier. This machine learning classification can help in pred icting the CPI and elapsed time and distinguish between AI and standard PC workl oads based on the profiled application(s) and captured profile metrics and event counts.”
NETL
NSTL