The future of Android will be a lot smarter, thanks to new programming tools that Google unveiled on Wednesday. The company announced TensorFlow Lite, a version of its machine learning framework that's designed to run on smartphones and other mobile devices, during the keynote address at its Google I/O developer conference. "TensorFlow Lite will leverage a new neural network API to tap into silicon-specific accelerators, and over time we expect to see [digital signal processing chips] specifically designed for neural network inference and training," said Dave Burke, Google's vice president of engineering for Android. "We think these new capabilities will help power a next generation of on-device speech processing, visual search, augmented reality, and more." The Lite framework will be made a part of the open source TensorFlow project soon, and the neural network API will come to the next major release of Android later this year.
A Tensor Processing Unit (TPU) is an Accelerator Application-Specific integrated Circuit (ASIC) developed by Google for Artificial Intelligence and Neural Network Machine Learning. With Machine Learning gaining its relevance and importance every day, the conventional microprocessors have known to be unable to effectively handle the computations be it training or neural network processing. The 1st Generation TPU is a hardware chip used at Google data center for faster computation. The 2nd generation TPU is now available in cloud and empowers businesses everywhere to access this accelerator technology to speed up their machine learning workloads using its high speed network. The 3rd generation TPU is twice as powerful as its previous generation and this result in an 8-fold increase in performance.
Embedded AI can transform a tabletop speaker into a personal assistant; give a robot brains and dexterity; and turn a smartphone into a smart camera, music player, or game console. Traditional processors, however, lack the computational power to support many of these intelligent features. Chipmakers, startups, and capital are taking this opportunity to the market. According to a Gartner report, the chip market's total revenue hit US$400 billion in 2017, and the figure is expected to exceed US$459 billion in 2018. Traditional chip makers are putting an increasing focus on AI chip development, venture capital is pumping significant investments into the market, and AI chip startups are emerging.
Back at Google I/O, CEO Sundar Pichai outlined the company's vision as an "AI first" company, with a new focus on contextual information, machine learning, and using intelligent technology to improve customer experience. The launch of the Pixel 2 and 2 XL, the latest batch of Google Home products, and the Google Clips offer a glimpse into what this long-term strategic shift could mean. We'll get to Google's latest smartphones in a minute, but there's much more to explore about the company's latest strategy.
Over the last years, the computational power of mobile devices such as smartphones and tablets has grown dramatically, reaching the level of desktop computers available not long ago. While standard smartphone apps are no longer a problem for them, there is still a group of tasks that can easily challenge even high-end devices, namely running artificial intelligence algorithms. In this paper, we present a study of the current state of deep learning in the Android ecosystem and describe available frameworks, programming models and the limitations of running AI on smartphones. We give an overview of the hardware acceleration resources available on four main mobile chipset platforms: Qualcomm, HiSilicon, MediaTek and Samsung. Additionally, we present the real-world performance results of different mobile SoCs collected with AI Benchmark that are covering all main existing hardware configurations.