AI and machine learning continue to unlock unprecedented efficiency and productivity across disciplines, especially in the world of the Internet of Things (IoT). Edge intelligence is a notable example of these technologies bolstering IoT computing and processing capabilities, specifically through emerging AI and ML subsets like Small Language Models (SLMs) and TinyML.
What is edge intelligence? It refers to the integration of AI and ML with edge computing, allowing devices at the edge of the network – such as IoT sensors, cameras and gateways – to interpret data, make decisions and act upon that data locally in real time, rather than relying on remote cloud servers.
That means edge intelligence enables IoT devices and systems to process and analyze data near its source, reducing reliance on cloud-based systems. This approach lowers network latency, decreases bandwidth usage, and improves overall system performance.
For example, consider FeverTags, a Texas-based cattle health data company. It offers solutions for round-the-clock monitoring of cattle health through low-powered, wireless ear tag sensors. These sensors can predict when a cow might be getting sick based on temperature data, allowing for early disease detection and containment long before visual observance.
Edge intelligence permits these temperature readings to occur locally without the need for a constant connection to the cloud. Moreover, farmers receive notifications and manage the health data for each animal remotely.
The differences between SLMs and TinyML
There are many potential applications of edge intelligence across sectors such as smart cities, health care and industrial automation. However, each application imposes unique requirements that often necessitate more specialized AI/ML technologies – namely, SLMs and TinyML.
SLMs are more compact versions of large language models (LLMs) used for NLP tasks such as text generation, summarization or chatbots. Their model sizes are smaller but typically larger than TinyML models. SLMs are relatively new but poised to become more prevalent as embedded devices with NPUs, such as the CCMP25, continue to evolve and improve.
TinyML is a sub-category of ML focused on ultra-low-power inference for real-time sensor data processing on microcontrollers or similarly constrained hardware. TinyML helps deploy ML models on tiny, low-power devices for real-time ML tasks like sensor data processing and speech recognition on embedded devices. These models are ultra-lightweight, often being only a few kilobytes in size.
TinyML enables IoT sensors to perform tasks like predictive maintenance and anomaly detection at the edge – especially important for battery-powered or intermittently connected environments. In manufacturing, for example, sensors running TinyML can predict an upcoming machine failure by, for example, analyzing vibrations or temperature fluctuations, and trigger timely alerts. These same models can detect operational anomalies, such as a motor running at an abnormally high temperature, reducing unplanned downtime and maintenance costs.
An exciting use case of edge intelligence on the horizon is multimodal sensor fusion. This involves combining data from different sensors (such as LIDAR, radar and temperature sensors) to improve predictions and decision-making at the edge by increasing perception, enhancing situational awareness and enabling faster, more accurate responses in mission-critical applications.
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