Alif Semiconductor and Telit Cinterion Unveil the Vision AppKit:

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Ultra-compact camera design can perform on-device AI use cases like face and object detection, image classification, and more at a significantly lower power consumption than previously possible for these use cases

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A Postage Stamp-Sized, Intelligent Connected Camera Platform

Telit Cinterion, an end-to-end IoT solutions enabler, and Alif Semiconductor, a supplier of the most secure, power-efficient Edge AI-enabled MCUs and fusion processors in the market, have announced the Vision AppKit — the world’s smartest and most efficient connected camera reference design. The Vision AppKit combines Telit Cinterion’s Wi-Fi and Bluetooth wireless technology or LTE-M communication modules in an ultra-compact camera design together with Alif Semiconductor’s Ensemble E3 series MCU, capable of performing on-device AI use-cases like face and object detection, image classification, and more at significantly lower power consumption than previously possible.

The Vision AppKit is a reference design for ultra-low power, small form factor AI-enabled camera that can capture images and/or video, perform AI-based processing in real-time on captured data, and deliver the results wirelessly to a display or other external system. Alif’s E3 Series MCU — known for its EdgeAI capabilities in battery-operated IoT devices — powers this groundbreaking design. Telit Cinterion supports communication in the Vision AppKit with the ME310 LTE Cat-M and WE310 Wi-Fi and Bluetooth Low Energy 5.0 modules.

The Alif Ensemble E3 series features a distinct High-Efficiency MCU core and a separate High-Performance MCU core, along with microNPUs that can be promptly enabled when a device needs additional AI/ML compute performance, to keep the overall system power footprint as low as possible. Alif Semiconductor’s aiPM™ technology dynamically powers only the logic that is in use at any given time thus achieving the lowest overall system power consumption. This combination delivers a performance uplift of at least two orders of magnitude compared to traditional 32-bit MCUs at a power consumption that is two orders of magnitude lower, drastically reducing inference times for AI camera vision-based tasks like object detection, face recognition, and image classification.

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