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Graphics processor designer Nvidia has teamed up with British chipmaker ARM Holdings to bring deep learning to various IoT devices and settings, according to VentureBeat.

The two semiconductor companies will integrate Nvidia's open-source Deep Learning Accelerator (NVDLA) into ARM's Project Trillium machine learning (ML) intellectual property suite.

This will give deep learning developers the high level of performance they need to easily integrate these advanced AI capabilities at scale into their IoT projects. Project Trillium, which was revealed last month, is a scalable set of processors designed to support various ML applications.

Deep learning is a subset of ML that uses neural networks — computing systems modeled after the human brain — to sift through big data sets to determine patterns and sequences that otherwise may not be found.

The partnership should help Nvidia power millions of very small IoT devices like smart meters or embedded sensors. Nvidia's chips and IP are commonly used in large internet-connected devices including drones, industrial robotics, and connected and autonomous cars.

It doesn't, however, currently offer many microchips or services for the small, embedded sensors that comprise the vast majority of the 10 billion IoT devices that are installed in the world, territory it has largely ceded to ARM, Intel, and others. While working with ARM doesn't guarantee that Nvidia will gobble up a large share of the market for powering these very small connected devices, it drastically increases its chances of doing so, according to a Moor Insights analyst note cited by VentureBeat. That's because ARM has major enterprise clients that have been using its architectures for chips for several years, relationships that Nvidia will likely be able to tap into.

ARM and Nvidia are industry leaders in ML and deep learning, and this move could push rival semiconductor companies to release similar deep learning offerings. Intel, for instance, has a particularly large IoT footprint — it earned $879 million in IoT revenue last quarter alone.

The computing giant could explore releasing new, more advanced ML and deep learning capabilities to try and fend off this advancement from ARM and Nvidia. Such a move would only further advance deep learning and ML applications within IoT settings, allowing companies of all sorts to leverage massive data sets to gain the insights needed to cut costs, boost operational efficiency, and grow their bottom lines. An oil and gas company could, for example, use deep learning to sift through the temperature data it collects from the sensors it outfits onto its refineries, helping it to recognize patterns and better determine what time of day its cooling systems need to be run.