Slidecast: ARM Steps Up to Machine Learning

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Jem Davies, VP Engineering and ARM Fellow

In this slidecast, Jem Davies (VP Engineering and ARM Fellow) gives a brief introduction to Machine Learning and explains how it is used in devices such as smartphones, autos, and drones.

“I do think that machine learning altogether is probably going to be one of the biggest shifts in computing that we’ll see in quite a few years. I’m reluctant to put a number on it like — the biggest thing in 25 years or whatever,” said Jem Davies in a recent investor call. “But this is going to be big. It is going to affect all of us. It affects quite a lot of ARM, in fact. The CPU group will be looking at the workloads, the neural network compute frameworks. And using that as examples to our performance analysis and benchmarking efforts to say well, what could we do to our CPUs to make them better at performing on those workloads. Equally, of course, once you step out of the CPU domain into something like the GPU area, then for us it’s all about non-generic workloads, so GPUs, graphic processing units. And of course, there’s a clue in the name there, are specifically, chosen to be very good at graphics. And it turns out the way that graphics is done efficiently is we have very parallel based workloads. We’re able to do lots of things at once. And graphics is, in fact, very computationally intensive. There’s lots and lots of arithmetic being performed in graphics. So it turns out that GPUs are actually very efficient at performing these sorts of workloads.”

In related news, ARM is introducing a free library of popular machine learning and computer vision routines optimized to run on ARM CPUs and GPUs.

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