The IoT’s Next Chapter:

Using Edge AI to Handle the New Wave of the IoT

The need for increasingly complex, potent, precise, energy-efficient, and sophisticated system solutions that can improve people’s quality of life is growing along with the market.

We are currently seeing the Internet of Things grow exponentially. There are already 127 devices being connected every second, and by 2027*, there will likely be 43 billion IoT devices. There is a growing need for more advanced, potent, precise, and energy-efficient system solutions that can improve people’s quality of life as this industry develops. Edge AI, one of the many essential technologies enabling this exciting IoT future, will contribute to enhancing IoT capabilities by enabling data analysis, predictive insights and intelligent decision making at the edge of the IoT.

Let’s start with the fundamentals: what exactly is Edge AI?

Although consumers and developers may already be familiar with machine learning (ML) and/or artificial intelligence (AI), they may not be as familiar with the specialized terms used in AI. The implementation and deployment of artificial intelligence (AI) applications in an edge computing environment or device near the data source, as opposed to a central location like a cloud computing facility, is known as edge AI.

When edge AI is used in practice, data is gathered from sensors and other sources, such trackers and health monitoring devices, processed locally on the edge device using an AI model, and the model’s output is then used to initiate a notice or initiate an action. Edge AI can support real-time use cases, conduct significantly faster inference, lower latency and network traffic, enhance privacy and security, and save energy by processing data locally.

Developers are currently considering a wide range of use cases and applications for edge AI, including wearables, health monitoring devices, appliances, and smart home systems that use facial and gesture recognition; predictive maintenance in factory automation; and security cameras that use AI to detect and handle suspicious activity in real-time, increasing efficiency and lowering costs.

Smart speakers and voice assistants that rely on a complicated suite of AI technologies for speech recognition analysis are common use cases for AI deployment. These include of employing automatic speech recognition (ASR) to turn sound waves into words, using Natural Language Understanding (NLU) to give those words meanings, and having the smart speaker reply by using Natural Language Generation (NLG).

Other trends include the smart home industry, where artificial intelligence (AI) is expected to boost edge device performance and offer seamless user experiences. Examples of such technologies include thermostats that learn users’ preferred temperatures, detect indoor and outdoor temperatures, and identify the people in the room; ovens that customize meals based on user preferences while maintaining safety by ensuring that only adults are able to use the device; and even vacuum cleaners that differentiate between different types of floors and maximize cleaning and battery efficiency. Each of these use cases makes use of sophisticated edge AI algorithms.

Edge AI is no different from other new trends or technologies in that it often presents new difficulties and important factors to be mindful of. As of right now, edge AI is most noticeable in the following domains:

• Greater Efficiency (and Low Power):

The IoT’s expansion necessitates the use of additional sensors, which in turn leads to more information sharing and device complexity, including a greater need for processing power. Now that the option of processing machine learning operations on the device itself has been added, this new generation of devices need both a neural net computing hardware accelerator and a high-performing CPU in order to do ML operations edge devices.

When power optimization is included in the mix, this need becomes even more difficult to meet in order to enable battery-operated and power-conscious end devices to function well while using less power. Positive: The edge device uses less energy to store data and perform algorithms than it would to transport everything to the cloud.

• Privacy and Security

Since less data is sent to the cloud and other external locations, edge AI devices handle the majority of their operations and data processing locally, which helps to mitigate security and privacy concerns. However, this does not mean that all of the data on an edge AI device is impervious to attacks, hackers, or security threats. Given the ongoing evolution of security assaults, a strong, appropriate degree of the data.

Absence of knowledge, time commitment, and thorough facilitation

Without the necessary knowledge, creating cutting-edge AI devices is nearly impossible. This can manifest as a lack of experience with software used in the creation and implementation of AI models, or as a lack of hardware understanding on the use of specific accelerators and processors designed for AI/ML.

Lack of experience and worries about time commitment may keep developers from selecting the optimal course of action or prohibit management from making the best choice.

Certainly, in these cases, having a well-rounded, robust hardware and software with an end-to-end toolchain solution provided by experts would help to reduce uncertainty, as well as speed up time to market for the next-generation of edge AI devices.

Infineon’s PSoCTM Edge addresses the edge AI challenges by introducing a new family of microcontrollers with a broad spectrum of capabilities including high performance, low power, state-of-the-art security and comprehensive enablement for a faster time to market.

This allows an IoT device to remain in deep sleep mode while being able to detect acoustic events or face detection actions and trigger actions so the system can fully wake-up, perform the task required and go back to sleep, maximizing energy efficiency resulting in longer battery life without sacrificing performance. Thus, edge AI does not only accelerate digitalization but also helps support decarbonization via power optimization.

As mentioned above, another key challenge is to safeguard data protection and minimize security threats. So, it becomes necessary for solution providers to equip their edge AI offerings with higher levels of security such that more secure devices are available for consumers.

As said, the growing complexity of the systemincluding the aggregation of sensors and the handling of complicated data at the edge is straining microcontroller performance boundaries. Simultaneously, low power consumption and excellent energy efficiency remain critical requirements in the IoT space.

With PSoCTM Edge, which offers high performance capabilities like a hardware-accelerated neural processor unit and a high-performance core, Infineon has introduced a multi-domain architecture approach to meet these requirements. It also supports increased energy efficiency with an ultra-low-power domain for Always-On applications.

Edge AI devices can be tampered and become a point of entry within the network and can become significant security threats. Furthermore, hackers can get hold of these Edge AI enabled devices broadly available in the market like thermostats, smart speakers or smart locks, analyze it for vulnerabilities and create malicious software to compromise the technology and the network. For these reasons, having a robust, right-sized embedded security architecture -like PSoCTM Edge has introduced – becomes paramount for these new wave of Edge AI devices.

Finally, Infineon is aware of how critical it is to have an Edge AI device on the market quickly. After acquiring Imagimob recently, Infineon is now able to provide an end-to-end machine learning platform that is highly flexible and user-friendly, with an emphasis on producing ML models that are suitable for production.

PSoCTM Edge offers the appropriate hardware, software, and tool offerings for a frictionless design experience and an accelerated time to market. It is equipped with robust ecosystem partners, extensive documentation, evaluation kits with connectivity and HMI modules, as well as the industry-recognized ModusToolboxTM software. It is also integrated with Imagimob’s edge AI development platform and its Ready Models for a faster and validated way to take ML-enabled microcontrollers to production.