Fleet tracking, asset tracking, autonomous vehicles, manufacturing automation and warehousing are all areas in which artificial intelligence-embedded chip technologies can offload network data-carrying loads. They can do this while providing frontline, real-time information.
Many of these on-the-go processes need lots of data to be activated. At the same time, they need this data in real time, and in transit, to take place. These kinds of processes don’t benefit as much from cloud computing as other data-intensive processes, such as training data through machine learning. Instead these processes benefit most from edge computing, which brings compute, networking and other resources directly to the devices and data that need them.
By activating artificial intelligence (AI0 processing loads at the level of a system-on-a-chip (SOC), IT can expand its options for distributing and offloading data-processing loads to different layers of enterprise architecture (e.g., cloud, a central data center, or the edge itself). This improves data management and processing. It also conserves bandwidth, and it expedites data and results.
SOC embedded micro-controllers use narrower memory and power consumption than that required by traditional GPUs (graphical processing units), FPGAs (field-programmable gate arrays) or other types of integrated circuits (ICs).
“We’ll see AI at the edge becoming commonplace in the next five years,” said Steve Conway, Hyperion research senior adviser, HPC Market Dynamics.
“ARM Atom, GPU and other embedded processors are already common in edge devices such as cell phones, sensors, automobiles, diagnostic medical imaging systems, gaming systems and many other devices. These established embedded processors will likely become the mainstream for supporting AI methods as these methods gain ground,” he said.
The Industry Impact of Edge IoT
In 2011 the term “Manufacturing 4.0” first appeared. It originated from the German government’s push to computerize manufacturing, and it introduced a future vision of digitalization, automation and artificial intelligence for factory production. In the scheme, edge technology could facilitate decisions at the locus of a problem or situation, where AI-embedded SOCs play major roles.
Today, this real-time edge decision making is real. Manufacturing processes are powered by AI-enabled decisions at the edge. In the future, an AI-enabled edge chip could send an actionable alert to purchasing about a shortage of raw materials, or alert sales about the possibility of a product shortfall if a deficient component is found.
Edge AI chip automation is also transforming logistics.
A truck convoy can cross-communicate with low-latency edge communications deployed to conserve fuel and optimize routes. Going forward, it will be possible for only one of these trucks to have a human driver, with the remainder of the convoy running on SOC-driven automation.
This could solve a major trucking industry issue: the shortage of qualified drivers. “This is one of the reasons you see so much technology coming into the trucking industry,” said Shelley Simpson, executive vice president, chief commercial officer, and president of highway services at J.B. Hunt Transport Services,
Perishable goods can also be monitored by intelligent sensors within each truck’s cargo compartment for temperature and humidity.
To read the complete article, visit IoT World Today.
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