A15 Bionic Beats Google Tensor Gets Machine Learning Benchmark Test
Last month when Google unveiled Pixel 6 and Pixel 6 Pro with the Google Tensor chip, it emphasised focus on the component’s capabilities with machine learning. Unlike the Snapdragon chips used on previous Pixel models, Google designed the SoC to work specifically with the computational photography and other features that the Pixel 6 line includes. In the latest test performed by tech website Notebookcheck, it was found that the Apple A15 Bionic chip crushes the Google Tensor at its greatest strength – machine learning. The test was done on the new Geekbench ML app that has been developed specifically to measure machine learning performance. The Google Tensor managed a score of only 307 in the Geekbench ML TensorFlow Lite CPU test, which was under a third of the Apple A15’s impressive score of 939. The 307 figure shows that it’s no match for Apple even after machine learning is the Tensor’s particular strength. The A15 also scores 2727 in the neural accelerator test, while the Tensor scores 1720. The Tensor closes the gap a tiny bit in the GPU test, scoring 1428, while the A15 responds with a higher score of 2727. The aggregate score of the A15 stood at 5,934, while Tensor got 3,455. This also shows that the A15 is around 71 percent faster than the Google Tensor in terms of machine learning tasks, at least in benchmark tests. Apple has been investing heavily in custom designing its chips for years now. On the other hand, the result does take some of the gloss off the Google Tensor and shows Apple’s dominance in raw chip performance.
The result takes gloss off the Google Tensor and shows Apple’s dominance in raw chip performance.
It was found that the Apple A15 Bionic chip crushes the Google Tensor at its greatest strength – machine learning.
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Victor is an aspiring Data Scientist &amp; is a Master of Science in Data Science &amp; Big Data Analytics. He is a Researcher, a Data Science Influencer and also an Ex-University Football Player. A keen learner of new developments in Data Science and Artificial Intelligence, he is committed to growing the Data Science community.