Technological advancements have influenced many areas including medicine, consumer electronics, aerospace and industry. One significant development is AI, which is impacting various parts of human life. Integrating AI into devices with limited resources like microcontrollers has been difficult. However, the creation of Tiny Machine Learning (TinyML) has made this integration possible.

Our latest whitepaper presents a comprehensive solution for deploying AI models on resource-constrained edge gateways, specifically microcontrollers. It highlights the transformative impact of AI across various fields and the challenges of integrating AI into devices with limited resources. The advent of TinyML has made it possible to deploy ML models on such devices, enabling real-time analytics at the edge and reducing cloud dependency.

The proposed AI solution involves two major phases: model development and model deployment. The model development phase uses Edge Impulse for tasks such as data collection, design, training and testing. The model deployment phase focuses on embedding the trained model into a reference architecture as an inference module.

The reference architecture is modular and incorporates the idea of small, autonomous services from microservices architecture. It utilizes a messaging pattern from the publish-subscribe architecture, where each task acts as a publisher or subscriber. This design ensures seamless communication and data exchange between tasks, enabling a flexible and modular system that can be easily maintained and scaled.

Read more to better understand this AI solution for efficient TinyML application development and deployment in constrained edge gateways!