Every day the world around us is getting smarter. From our homes to industry, small smart devices are making real-time decisions and adapting to the environment. This is all made possible by Embedded AI that is changing the landscape of the IoT and edge devices. If you are asking yourself how these little yet powerful technologies are changing the future, you are in perfect space.

Key Takeaways

• Embedded artificial intelligence employs intelligent algorithms on an IoT edge device and makes almost immediate decisions without utilizing the cloud. This results in lower latency, security enhancements, reduced bandwidth usage, and increases device autonomy.
• Embedded AI on specialized chips and software applications allows AI features to reside in resource-constrained devices like sensors and wearables.
• Applications for embedded AI are already taking place in the real world. Examples exist in smart homes, healthcare, industrial automation, and autonomous vehicles.
• Obstacles remain in shrinking hardware footprints and securing the data from sensors, but advancements being made within the technology ecosystem continue to provide additional opportunities for growth within this emerging category.

What is Embedded AI and How Does It Differ from Traditional AI?

Before understanding how Embedded Ai interacts with IoT, let’s clarify what it is.

Embedded AI refers to AI functionalities operating directly on the actual hardware from IoT and edge devices (for example, microcontrollers and sensors). Unlike traditional AI (primarily using the cloud) that operates in the cloud, Embedded AI processes data locally on the device it is operating on.

Specifically traditional cloud-based AI works by taking your data, placing it in the cloud or on a powerful server, and processing the data. Also called inference. This takes time because you are creating delays in interpreting the data. The delays create privacy issues, worry and concerns with data staying on the cloud and not being shared. Embedding Ai accepts the fact that IoT handles data where it is created, utilizing low latency and keeping the data as secure as possible because it is not generating data the entire time or putting its data to the cloud enabling more bandwidth to be free.

Embedded Ai also utilizes technologies like:

Neural Processing Units (NPUs): Specialized chips designed to maximize the effectiveness of AI.
Lightweight AI models: Narrowed-down AI algorithms intended for low-power devices.
AI Accelerators: Any hardware component which speeds AI computations.
Therefore, embedded AI is what drives smart devices that do not rely on the internet to work.

How Embedded AI is Revolutionizing IoT Devices and Edge Computing

In the past few years, the Internet of Things (IoT) has exploded, connecting millions of things from sensors, cameras and appliances. Many of these devices traditionally collect data and send to the cloud for analysis, which takes time and can add expense.

Embedded AI improves this process by allowing devices to make real-time or instantaneous decisions and analyze data locally. This can diminish or eliminate congested networks, conserve bandwidth and minimize reliance on the cloud. A smart traffic light installed in a city is a good example that can immediately adjust based on real-time sensor information. In the industrial space, embedded AI can detect machine faults early, avoiding hundreds of thousands of dollars in repairs and lost production time. Autonomous vehicles operate based on embedded AI that rapidly gathers data from many sensors to sediment a safe navigation path.

Applications that benefit from embedded AI include:
Smart homes: Voice assistants and adaptive appliances that react instantly, and you have the option to it when it comes to privacy.

Healthcare wearables: Devices that monitor heart rate or activity and provide users alerts in real-time if things are wrong.

Industrial automation: Sensors can detect a problem with the machine so it can troubleshoot the issue before they fail.

Automotive: Edge AI means that the car can perceive it’s surroundings and make safer driving decisions in real-time.

Technological Advances Driving Embedded AI in IoT and Edge Devices

Embedded AI is at last ready to be viable on small devices based on several technological advancements.
Specialized AI chips: Many devices such as NPUs have been developed to perform AI (deep) learning tasks and consume low power.
Optimized AI models: Several models have been developed to operate on low resources such as MobileNet and TinyML.
Edge AI platforms: More robust software platforms are available to create and deploy AI on embedded systems such as TensorFlow Lite and PyTorch Mobile.
Improved security: Processing data locally provides more privacy; privacy from further hardware encryption and secure boot has improved security overall.

Embedded AI is a fast growing market – while the landscape is still in its infancy stage, it is anticipated to grow rapidly into the billions – as will the overall demand for “smarter” and more autonomous edge devices.
 As mentioned above, embedded AI is an evolving ecosystem which comprises hardware, AI model optimization at the base model level, software for deploying AI, and improving security levels for edge devices. Together, these advancements forge a realm where embedded AI can be executed with even low power, un-optimized IoT devices and wearables.

Key Benefits of Embedded AI for IoT and Edge Devices

Embedded AI offers a number of revolutionary benefits:
Lower latency: Speed is critical for applications such as autonomous vehicles and medical devices. There must be instant responses.
Improved data privacy: Privacy is more secure because the data is processed on device and is less likely to leak sensitive information.
Cost savings: Less data sent over networks will create lower bandwidth and cloud costs.
More autonomy: Devices must operate independently even if there is little to no reliable internet connection.
These advantages contribute to more reliable, secure, and intelligent IoT systems.

Prominent Applications of Embedded AI in IoT and Edge Devices

Embedded AI is already proving its potential in several areas:

Smart homes: Voice appliances and energy-efficient lighting are adjusting to needs dynamically in real-time. Healthcare: Wearable technologies sensing every second and sending alerts if something abnormal arises.
Industrial automation: Machines monitor themselves to predict and alert maintenance before a breakdown.
Automotive: Embedded AI provides rapid sensor fusion in order to mitigate accidents and allow for autonomous driving.

Conclusion

Companies are challenging employees to keep pace with the embedded AI revolution that is remapping the landscape of IoT and edge devices, but this requires training. Training employees is key to helping employees gain core competencies, tackle tough problems, and confidently employ the latest AI toolsets.
 All organizations that value innovation and security should invest in ongoing in house corporate training. Cranes Varsity offers a portfolio of experiential training to learn and master embedded AI.