{"id":3770,"date":"2025-08-19T07:32:55","date_gmt":"2025-08-19T11:32:55","guid":{"rendered":"https:\/\/blogs.mathworks.com\/matlab\/?p=3770"},"modified":"2025-09-03T09:27:13","modified_gmt":"2025-09-03T13:27:13","slug":"deep-learning-training-in-matlab-has-just-gotten-faster-on-apple-silicon-macs","status":"publish","type":"post","link":"https:\/\/blogs.mathworks.com\/matlab\/2025\/08\/19\/deep-learning-training-in-matlab-has-just-gotten-faster-on-apple-silicon-macs\/","title":{"rendered":"Deep Learning training in MATLAB has just gotten faster on Apple Silicon Macs."},"content":{"rendered":"
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MATLAB R2025a has some great news for Apple Silicon Mac users: Apple Accelerate is the default BLAS<\/a>. Since Apple Accelerate makes use of various hardware features in Apple Silicon CPUs, this means things like matrix-matrix and matrix-vector multiply are much faster. So I wondered....would this lead to a measurable improvement in Deep Learning training?<\/div>\r\n
I had some code lying around for training a simple CNN model<\/a> on the CIFAR-10 dataset so thought I'd give this a try.<\/div>\r\n
I train for one epoch on all 50,000 images in the cifar10Test<\/span> set and have some data augmentation<\/a> going on too. Training for just one epoch means that the model is far from completely trained but I'm not interested in that, I just want to see how quickly I can get through the epoch.<\/div>\r\n
Here are times for running my benchmark training script 3 times in a row on my 10 core M2 Macbook Pro.<\/div>\r\n