Three in four phones to have dedicated AI chips by 2022

Qualcomm is offering an AI Tensor Accelerator in the Snapdragon 855's Hexagon DSP for the first time.

Published: 12th April 2019 06:49 PM  |   Last Updated: 12th April 2019 06:49 PM   |  A+A-


For representational purposes only.


NEW DELHI: Three out of four smartphones are forecast to have dedicated Artificial Intelligence (AI) processors by the end of 2022, a Counterpoint Research report said on Friday.

Sales of AI smartphones would increase to 1,250 million units in 2022 from 190 million in 2018, representing more than three-quarters of all smartphones shipping in that year.

"We see voice assistants as one of the first applications to benefit from device-based processing," Gareth Owen, Associate Research Director at Counterpoint Research, said in a statement.

Huawei and Apple were the first original equipment manufacturer (OEMs) to include dedicated AI processors in their system on chips (SoCs) (Kirin 970 and A11 chips, respectively) launched in their flagship handsets in September 2017.

Two years on, virtually all other SoC vendors are following suit. For example, Qualcomm is offering an AI Tensor Accelerator in the Snapdragon 855's Hexagon DSP for the first time, the report said.

The key benefits of this are higher AI processing performance and lower power consumption. However, this must be balanced against the actual need for AI processing, which until recently has been limited, it added.

"Today, most voice processing in smartphones is Cloud-based. However, voice assistants will be able to process commands quicker and respond faster with on-device processing. It also resolves privacy concerns," Owen added.

Smartphones have been leveraging the capabilities of AI for some time. However, till now the processing has been done either in the Cloud or distributed across the various computer chips in devices such as CPUs, GPUs and DSPs.

As AI becomes part of the mobile experience, smartphone system-on-chip (SoC) vendors are racing to improve the machine learning (ML) capabilities of their chips by integrating dedicated AI processing cores into their designs.



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