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Arm portrays its strategy for machine learning at the edge

digitimes.com, May. 29, 2019 – 

With one of the strongest processor ecosystems in the industry, Arm's role to accelerate the adoption of machine learning (ML) at the edge can't be overemphasized, and its effort to enhance its CPU performance and software support for ML workloads is now in full swing. According to Jem Davies, Arm fellow, VP and GM, Machine Learning Group, the market outlook, challenges, and Arm's overall strategy to address the huge opportunity are clearly disclosed.

ML in edge devices is just beginning

"We see ML as one of the most exciting advancements in computers and processors in modern times," Davies said. "As machine learning is exploding across edge devices, we're now to the point where we are seeing huge amounts of activities and some really interesting use cases across all markets Arm technology services."

From his point of view, some of the most interesting use cases and active communities for ML are coming out of the IoT sector using traditionally very small processors like the Arm Cortex-M microcontroller family.

For example, the use cases come from the embedded and IoT spaces, such as life improving medical devices like smart asthma inhalers, through to industrial sorting and robotics to voice assistants, more intelligent home security and even things like DTVs where there is a lot of activity in super scaling, scene recognition and picture quality enhancement and gesture recognition.

Of course, there are some of the more interesting ones, including the well covered autonomous vehicle and driver assistance; in smartphone a huge range of applications are implementing ML improvements like smarter games engines, richer social media applications, and even utility applications built directly into the OS like predictive text and voice assistants.

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