GoogleGOOG has become a full stack AI company. It uses its own data to train its own algorithms running on its own chips deployed on its own cloud. The result is that Google is able to innovate very quickly in AI and deploy it ahead of its peers—just like AppleAAPL took full control of OS and chip development for the iPhone. By becoming world-class at four layers of the tech stack, Google’s AI is now harder to catch than ever.
While other companies like FacebookFB and MicrosoftMSFT also have created novel deep learning algorithms, Google’s research breadth and user scale are unmatched in the industry. By publishing the results, Google further cements its reputation as the leader in AI, attracting the next wave of talent and thus creating a fly-wheel effect in algorithm development.
In 2016, Google announced that it had built a dedicated processor for deep learning inference called the Tensor Processing Unit (TPU). The TPU provides high performance in deep learning inference while consuming only a fraction the power of other processors.
Google’s first generation TPU can perform 92 trillion operations per second and consumes 75 watts of power: its per watt performance equates to 1.2 trillion operations per second. In comparison, NVIDIA’sNVDA latest Volta GPU performs 120 trillion operations per second but uses 300 watts of power: its per watt performance is only 0.4 trillion operations per second­. In other words, Google’s TPU is 200% more power efficient than NVIDIA’s GPU for the same performance, as shown below.
Google’s decision to bring chip design in-house has a direct parallel in the smartphone market. In 2010, Apple switched from third-party to Apple-designed System on Chips (SoCs) for its iPhone and iPad products, giving it a significant performance and go-to-market advantage over competitors. For example, in 2013 Apple designed and shipped a 64-bit ARM SoC a full generation ahead of competitors, nearly doubling the iPhone 5S’s performance relative to other smartphones while enabling new security features like Touch ID.
In the cloud computing business, specifically Infrastructure-as-a-Service (IaaS), Google currently is in third place, behind AmazonAMZN and Microsoft. One space in which Google leads, however, is cognitive application programming interfaces, or APIs.
[i] Image classification accuracy versus AlexNet from 2012. In terms of complexity, GoogleNet has 12x fewer parameters than AlexNet.