Embedded Machine Learning for IoT: Top Predictions for 2021

The internet of things (IoT) is unprecedently changing the way people live and work. It is booming at a rapid pace and the domains where IoT is evolving are as diverse as its applications, ranging from decision control to the exploration and discovery of new information. It is predicted that there will be around 175 zettabytes of data generated worldwide by 2025. Companies who already invested in IoT will gain a massive competitive advantage and new opportunities through such amounts of data. Applying embedded machine learning for IoT devices can also deliver several new capabilities as it has the potential to lessen data transmission payload and integrate Low-Power Wide-Area Network (LPWAN) technologies, which offer wide range connectivity while providing a long battery life.

Now as businesses are approaching a new era of innovation, various new abilities of embedded machine learning on IoT devices will occur in 2021. Here are the top embedded ML for IoT predictions for 2021.

Smarter Chips Will Enable Intelligence on IoT Sensors

Building an IoT solution using legacy approaches faces numerous challenges and one of them is to connect millions of IoT devices to the cloud across distinct sensors, actuators, operating systems, compute power, and others. The amount of data currently being gathered by IoT is already huge, and it is expected that it will get far bigger and more interesting. The shipment of more powerful IoT AI chips and more domain-specific IoT AI chips will enable smarter intelligence on IoT sensors, according to Hiroshu Doyu, an embedded AI researcher at Ericsson.

Embedded ML to Revolutionize Manufacturing

According to IDC, 20% of leading manufacturers will rely on embedded intelligence by 2021, using AI, IoT, and blockchain applications to automate processes and intensify execution times by up to 25%. Embedded machine learning can identify certain areas that need to be efficiently tested and provide manufacturers with information to better predict the maintenance and equipment failure issues to prevent any uncertainty in the future. Embedded ML solutions can also automate manufacturing processes entirely along with smart manufacturing operations. They can unlock sensors’ data that is currently discarded due to cost, bandwidth, or power constraints.

Enterprise Maturation Through Smarter Chips

Business decision-makers these days are entirely overwhelmed by the technology which has been already embedded. These embedded technologies are becoming a new trend across businesses owing to the availability and generation of massive digital data daily. However, the problem is connecting to it and absorbing it. The adoption of more intelligence at the device level may address the issues associated with bandwidth and latency. Lucy Lee, a senior associate at Volition Capital who tracks embedded AI/ML on IoT predicts that many more autonomous chips are expected to be made this year.

AI/ML Acceleration

The paradigm shift towards digitization is giving businesses a huge opportunity and enabling them to garner greater benefits and ROI. Advancements in artificial intelligence and the introduction of application-specific custom AI chips are now allowing businesses to glean real-time data about their business processes and their customers. As the AI chips market is gaining visibility across industries worldwide, it is expected to grow at a CAGR of over 42% during 2020-2024. The adoption of AI chips in data centers will expedite its growth.