ARM wants to provide machine learning to consumer devices

The chip company works on solutions to implement machine learning in consumer devices, in order to popularize technology

Artificial intelligence seems reserved for high performance computing. And the more sophisticated their different disciplines, such as machine learning, the more pronounced this phenomenon seems. However, little by little this technology reaches more and more users from new devices and new networks.

To carry out artificial intelligence tasks today, you only need a good Internet connection, some computing capacity and access to an AI system in the cloud. Even some device manufacturers have struggled to make their products contain chips optimized for artificial intelligence workloads. This is the case of the NPUs (neural processor unit) that already include high-end smartphones from Apple, Huawei or Samsung.

But the truth is that artificial intelligence still has a way to reach the whole world. For the ARM chip developer this is something that can be achieved. And the company works on it, according to an interview with his vice president of machine learning Steve Roddy.

The company’s vision for the future is to create NPUs, not only for high-end mobile devices, but for everyone. In this way, the processing capacity offered by one of these chips, especially in fields such as the recognition of objects through artificial vision, will be available in mid-range terminals and even in the lower range.

Machine learning en los dispositivos de consumo

Obviously this takes time. ARM works on optimized processors that can incorporate machine learning in consumer devices. This type of chip would be complementary to the CPUs (central processor unit) and to the GPUs (graphical processor unit).

AI for everyone
As a branch of artificial intelligence, machine learning also provides great capabilities in the sphere of the consumer market. The fact that devices sold massively can access this technology would also improve some functions that now depend on the cloud.

As a demonstration of the usefulness of machine learning in a consumer device, voice recognition can be cited. Natural language processing is a task that consumes a lot of resources. And normally a local computer system, such as the processor of a smartphone or a computer, is not prepared to deal with the arbitrariness that such a request can reach.

Having a processor optimized for machine learning will, for example, improve the operation of voice translators without an Internet connection. Something quite useful when you are in a foreign country where neither your language nor your data rate allows you to communicate.

Images: Free-Photos, SplitShire

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