embedded-machine-learning
Technology

Learn about Embedded Machine Learning for IoT and the most important predictions for 2021

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What new developments will embedded machine learning for IoT add to the industry?

The internet of things (IoT) is transforming how people live and function in unprecedented ways. It’s growing at a breakneck pace, and the fields in which it’s developing are as diverse as its applications, ranging from decision-making to the exploration and discovery of new data. By 2025, it is estimated that 175 zettabytes of data would have been produced worldwide. Companies who have already invested in IoT would gain a significant competitive advantage and new prospects as a result of the vast amount of data available. Embedded machine learning for IoT devices will include a number of new capabilities, including the ability to reduce data transmission payload and incorporate Low-Power Wide-Area Network (LPWAN) technologies, which provide wide-area networking with long battery life.

Various new capabilities of embedded machine learning on IoT platforms will arise in 2021, as companies approach a new age of innovation. The top embedded machine learning for IoT predictions for 2021 are listed below.

IoT Sensor Intelligence Will Be Enabled by Smarter Chips
Connecting millions of IoT devices to the cloud through different sensors, actuators, operating systems, processing resources, and other factors is one of the challenges of building an IoT solution using legacy approaches. The amount of data collected by IoT is already enormous, and it is expected to grow much larger and more interesting in the future. According to Hiroshu Doyu, an embedded AI researcher at Ericsson, the shipment of more powerful IoT AI chips and more domain-specific IoT AI chips will allow smarter intelligence on IoT sensors.

Manufacturing to be Revolutionized by Embedded Machine Learning
According to IDC, 20 percent of leading manufacturers would use embedded intelligence by 2021, automating processes and reducing execution times by up to 25% using AI, IoT, and blockchain applications. Embedded machine learning will detect areas that need to be thoroughly tested and provide manufacturers with knowledge that will help them better anticipate maintenance and equipment malfunction problems in the future, reducing risk. Embedded machine learning systems can also fully automate manufacturing processes, as well as smart manufacturing operations. They can unlock data from sensors that has been discarded due to cost, bandwidth, or power limitations.

Smarter Chips for Enterprise Maturity
Today’s business decision-makers are fully exhausted by the technology that has already been implemented. Because of the abundance and generation of large digital data on a daily basis, these embedded technologies are becoming a new trend among businesses. Connecting to it and absorbing it, on the other hand, is a challenge. The adoption of more intelligence at the system level can help to solve bandwidth and latency issues. Many more autonomous chips are expected to be made this year, according to Lucy Lee, a senior associate at Volition Capital who monitors embedded AI/ML on IoT.

Machine Learning/Artificial Intelligence Acceleration
Businesses have a tremendous opportunity to gain greater benefits and ROI as a result of the paradigm change toward digitization. Artificial intelligence advancements and the advent of application-specific custom AI chips have allowed companies to gather real-time data about their business processes and customers. The AI chips market is projected to expand at a CAGR of over 42 percent between 2020 and 2024, as it gains traction across industries around the world. The adoption of AI chips in data centres would hasten the development of the industry.


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