Intelligence at the edge is increasingly being deployed, often in tandem with AI, to enable lower-latency decision-making on devices. This saves the delay associated with communicating data to a central server and then communicating an actionable response for the device to follow. It also saves on the costs of connectivity.

By building more intelligent devices, latency can be minimized, and intelligent edge capabilities are being adopted to achieve this in devices such as smart farming sensors, performance monitoring devices in industry, and to add on-device intelligence to robots, wearables, laptops, and smartphones. Functionality such as battery optimization in EVs, video calling, photo-tagging, and AI-enabled cameras can be added, enabling devices to do more.

The benefits of adopting edge intelligence in IoT go well beyond operational efficiency as scalable, resilient, and responsive systems are created. Edge devices distribute decision-making capabilities, so if one unit fails, the network of devices remains operational. This enhanced resilience is making this approach suitable for critical infrastructure like power grids, healthcare facilities, and smart city environments.

Edge intelligence IoT can also enable highly personalized user experiences. Smart home devices can tailor interactions to individual user preferences in real-time, and retail kiosks can recognize returning customers and offer customized product suggestions.

Low latency is vital in systems that require split-second decisions to mitigate hazards, and industrial automation systems can respond quickly to sensor inputs, helping to prevent equipment failures and downtime. Privacy and security are also enhanced since sensitive data is kept on-site rather than sent to the cloud. Healthcare monitors, for instance, can store patient data locally, while security systems can keep video feeds within premises. This builds user trust while helping to resist data breaches and ensuring compliance with privacy laws.

There are, however, a series of challenges for developers to overcome to ensure edge intelligence performs as expected, and issues such as crashing apps, excessive memory consumption, incorrect results, inability to quantize, and others have been encountered as developers design in CPUs, GPUs, and NPUs in support of AI. Hardware and software tools to assist in the development of optimized intelligent edge devices are therefore needed.

The Quectel PI-SG565D smart MOB development board is a single board computer (SBC) ideal for edge computing applications. Quectel has engineered the PI-SG565D SBC to meet the rigorous demands of industrial automation, smart retail, robotics, and consumer electronics. Its dimensions are optimal for embedded applications, and its lifecycle extends to 2036. The board’s combination of powerful computing, rich connectivity, and scalable design positions it as a future-proof solution for developers seeking to build innovative, high-performance systems in an ever-evolving technological landscape. 

Qualcomm’s Edge Impulse development platform, which brings intelligence to edge devices, empowers machine learning teams to run edge computing at peak performance on any edge hardware with unmatched ease and speed. The platform enables developers to accelerate the building, delivering, and optimizing of embedded solutions using real-world data.  

The Quectel PI-SG565D SBC and the Edge Impulse platform have been introduced in a recent Quectel Masterclass. The Masterclass, titled ‘Introduction to edge computing with the PI-SG565D SBC and Edge Impulse Platform’, is presented by Zeljko Maric, the product development manager at Quectel, and Joshua Buck, the senior solutions engineering manager at Edge Impulse.