Google is taking two steps \u2013 one in hardware and one in software \u2013 to bring its analytics and machine learning capabilities to edge networks and even to individual Internet of Things (IoT) devices to better deal with the data generated by a growing number of IoT devices, the company said at its Cloud Next technology conference.\nThe first step is Google extending the features of its Cloud IoT software platform to edge networking. The second is a tiny chip that could be integrated in IoT devices themselves and process the data they collect before transmitting it.\n\nEdge computing \u2013 which describes an architecture where a specialized computer sits very near to the IoT endpoints themselves to perform analysis and data processing from those endpoints, as opposed to sending that information all the way back to the data center \u2013 is very much the up-and-coming model for IoT deployment, particularly in use cases that have strict requirements around latency.\nHowever, Christian Renaud, IoT research director for 451 Research, said the technical advantages of edge computing are secondary to a much more human factor \u2013 managers of the operational technology implementations (factory floors, fleets of vehicles, etc.) that stand to benefit from IoT tech simply aren\u2019t comfortable moving important management and analytics functionality to a cloud, even a private one.\nGoogle bringing its IoT software stack to edge devices, therefore, removes a potentially serious barrier to entry and makes the company a more attractive option for enterprise IoT management. It also brings Google\u2019s offering more into line with its competitors in Microsoft and Amazon, who have already brought their IoT platforms to the edge.\nGoogle\u2019s standing among its competitors might be helped by the chip it announced at Cloud Next called Edge TPU that accelerates machine learning via the company\u2019s TensorFlow AI software.\n Google \n\nGoogle Edge TPU on a penny coin\n\n\n\u201cDepending on the effectiveness of Edge TPU, this may have not only caught them up, but allowed them to pull ahead of the competition technically,\u201d said Renaud.\nEdge TPU a custom chip just a fraction of the size of a penny that\u2019s designed specifically to run Google\u2019s TensorFlow Lite machine-learning models on endpoint devices. The idea is to use IoT devices themselves to generate meaningful predictions and insight.\nGoogle says on its website that the chip could be used to enable predictive maintenance, anomaly detection, machine vision, robotics and voice recognition, among other things.\nDevelopers who want to get their hands on one early can apply to Google here.\nIt remains to be seen whether Edge TPU will see large-scale uptake and whether it works as well as Google says it does.