Monday, February 26, 2024

Machine Learning Solutions For Smarter Edge Devices

By Nidi Agarwal

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The machine learning models for edge devices offer rapid deployment, high performance, and seamless integration for enhancing smart devices.

ML

Imagimob has launched IMAGIMOB Ready Models, a pre-built machine learning (ML) solution designed for rapid deployment and high-performance on edge devices. These Ready Models are engineered to be robust and efficient, tailored for seamless integration with existing microcontroller (MCU) platforms like PSoC 6. This launch aims to streamline the process of bringing smart devices to market, eliminating the need for extensive time, cost, and specialized knowledge typically associated with custom ML development.

The four audio-based Ready Models include Baby Cry for baby monitors, Siren Detection for pedestrian safety, and Coughing Detection and Snoring Detection for wearable devices in the medical and health sectors. The ready models require minimal or no engineering and AI expertise for implementation. With all the development and testing already completed, these models provide a direct and efficient route to the market.

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Ready Models improve products for end customers by incorporating machine learning models, which increase reliability and provide peace of mind. These models also add new features to existing devices. They are compatible with wearables, health, and safety devices, making them accessible and convenient for end users.

“If you look at the Edge AI space right now, you can probably count on one hand how many companies provide off-the-shelf models for any one solution,” says Sam Al-Attiyah, Head of Customer Success at Imagimob “Our Ready Models are built upon eight years of expertise and thoroughly tested out in the field in different environments, so they are validated in terms of performance. And the fact that we are running them on small edge devices is unique.”

The company claims the models are tested in different scenarios worldwide to ensure they work with no bias based on specific geographies or ethnicities. The result of all these efforts is a model that performs precisely as desired when integrated into their products.

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