Future Of Machine Learning On Smartphones

#News(IoTStack) [ via IoTGroup ]

Headings…
Future Of Machine Learning On Smartphones
How The Adjustments Were Made To Meet The Demand
What Do Experts Have To Say
Deep Learning Is Just A Touch Away
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These handheld devices are the epitome of software and hardware engineering; and to do these tasks, they require state-of-the-art image recognition and NLP models running in the background.
For Portrait mode on Pixel 3, Tensorflow Lite GPU inference accelerates the foreground-background segmentation model by over 4x and the new depth estimation model by over 10x vs CPU inference with floating-point precision.
Apple says that it is using machine learning in the iPhone 11’s cameras to help process their images, and that the chip’s speed allows it to shoot 4K video at 60 fps with HDR.
In 2015, Qualcomm kick-started the deep learning on mobiles movement with its efforts to accelerate models using mobile GPUs.
havng said that,The use of floating-point and quantized models for mobile devices has been a topic of discussion amongst the developers and vendors.
For this comparison, the Mobile devices were running the FP16 model using TensorFlow Lite and NNAPI.
In this work, they evaluated the performance and compare the results of all chipsets from Qualcomm, HiSilicon, Samsung, MediaTek and Unisoc that are providing hardware acceleration for AI inference.
TensorFlow Lite is still only one major mobile deep learning library, providing reasonably high functionality and ease of deployment of deep learning models on smartphones.
The enhancement of their hardware services combined with state-of-the-art software options has put Apple at the frontiers of machine learning advancement.
With all SoC vendors and phone makers like Apple and Samsung, determined about AI for mobiles, running many state-of-the-art deep learning models on smartphones in the last few years have radically changed.
Today devices having Qualcomm and other top systems on a chip (SoCs) come with a dedicated AI hardware designed to run ML workloads on embedded AI accelerators.
At the TensorFlow’s developer summit, held earlier this year, along with TensorFlow 2

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