One of the most highly anticipated developments in the IoT is the infusion of Artificial Intelligence and Machine Learning (AI/ML) in edge devices. By making IoT devices trainable, actionable, and capable of extracting information and learning from the environment, they become more contextually aware and ultimately more useful in a variety of ways. Silicon Labs’ wireless SoCs and MCUs support the use of AI/ML, enabling developers to add capability, reduce complexity and take advantage of low-power, low-cost, small-footprint design.
Edge device sensors can generate vast quantities of raw data, and therefore occupy large amounts of bandwidth. AI end nodes can pre-process data help reduce bandwidth usage.
Specialized AI modeling software create models that are used by small application MCUs, thus avoiding complicated coding typically required to detect subtle differences in raw data.
AI adds functional benefits and capabilities but without adding to the memory footprint or MCU requirements since code size tends to be reduced. Local processing, also reduces current as communications are reduced.
With reduced datasets transfering to the cloud, bad actors have less data on which to engage in hacking activity. Smaller datasets enabled by AI/ML also help neutralize the ability for hackers to identify data patterns.
The Thunderboard Sense 2 offers developers a compact feature-packed reference design. The kit was designed to provide a fast path to develop and prototype IoT products such as wireless sensor nodes. Being rich in a broad range of sensors makes it an ideal platform to illustrate the power of Artificial Intelligence in a small platform IoT device. Thunderboard Sense 2 is fully supported by the industry-leading Simplicity Studio tool suite and comes complete with a fully supported on-board J-Link debugger.
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