首页|Gulf University for Science and Technology Researchers Update Knowledge of Machi ne Learning (Characterization and Machine Learning Classification of AI and PC W orkloads)
Gulf University for Science and Technology Researchers Update Knowledge of Machi ne Learning (Characterization and Machine Learning Classification of AI and PC W orkloads)
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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.”
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