The internet of things has been a rapidly growing segment of technology over the past decade. Ever since Apple took made the smartphone a consumer success with its first iPhone, users have grown comfortable carrying technology in their hands and pockets. This IoT-filled world has created new opportunities and challenges.
According to IDC, connected devices will generate over 40 trillion gigabytes of data by 2025. This is too much of a good thing, especially if IoT devices remain only collectors and not processors. To help speed up data collection, Google has announced its Cloud IoT Edge platform, as well as a new hardware chip called the Edge tensor processing unit.
What are Google's new announcements?
Google described its decision to move forward on the Cloud IoT Edge platform as "bringing machine learning to the edge." Essentially, current edge devices, such as drones and sensors currently transmit most of their data collection back for internal processing. This procedure uses a lot of bandwidth and reduces the speed at which decisions can be drawn from the data. It also places a lot of stress on constant network connectivity, as any downtime can result in lost information.
Google's new software solution would allow this data processing to happen right at the data source. It will also enable advanced technology, such as machine learning and artificial intelligence, to operate on these edge devices. Enter the Edge TPU: This chip is designed to maximize performance per watt. According to Google, the Edge TPU can run TensorFlow Lite machine learning models at the edge, accelerating the "learning" process and making software more efficient faster.
How does this compare with the greater market?
In this announcement, Google is following in the path of Microsoft. Released globally in July, Azure IoT Edge accomplished many of the same tasks that the Cloud IoT Edge solution intends to. The two aim to empower edge devices with greater machine learning performance and reduce the amount of data that must be transmitted to be understood.
However, as Microsoft has been in the hardware space much longer than Google, no TPU chip needed to accompany the Azure IoT Edge release. It is possible that Google may gain an advantage by releasing hardware designed to optimize its new platform performance.
Amazon's AWS Greengrass also brings machine learning capabilities to IoT devices. However, unlike the other two, this platform has existed for a while and seen modular updates and improvements (rather than a dedicated new release).
The presence of all three cloud platform giants in edge space signifies a shift to at-location data processing. Cloud networks have already been enjoying success for their heightened security features and intuitive resource sharing. As these networks become more common, it has yet to be fully seen how Microsoft, Amazon and Google deal with the increased vulnerabilities of many edge devices. However, with all three organizations making a sizeable effort to enter this market space, businesses should prepare to unlock the full potential of their edge devices and examine how this technology will affect workflows and productivity.