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Expanded MCU portfolio optimized for machine learning at the edge

April 4, 2024 By Aimee Kalnoskas Leave a Comment

Infineon Technologies AG has unveiled the latest addition to its product lineup with the introduction of the PSOC™ Edge family of microcontrollers (MCUs) tailored specifically for machine learning (ML) applications. The release includes three distinct series, namely the E81, E83, and E84, each offering a flexible and compatible array of performance levels, features, and memory options. Equipped with comprehensive system design tools and software support, developers can seamlessly transition from idea to market-ready product, empowering the realization of innovative ML-enabled solutions for the Internet of Things (IoT), consumer electronics, and industrial sectors.

At the core of Infineon’s PSOC Edge E81, E83, and E84 microcontrollers lies the robust Arm Cortex®-M55 architecture, augmented with Helium™ DSP support alongside Arm Ethos™-U55 and Cortex-M33 integrated with Infineon’s cutting-edge NNLite technology—a proprietary hardware accelerator engineered for neural network acceleration. These MCU series boast a rich assortment of peripherals, on-chip memory, and robust hardware security features, supplemented by diverse connectivity options, encompassing USB HS/FS with PHY CAN, Ethernet, WiFi® 6, BTBLE, and Matter protocols.

Designed to cater to a myriad of applications, the PSOC Edge E8x series finds utility in Human Machine Interface (HMI) applications within appliances, industrial machinery, smart home systems, security devices, robotics, and wearables. Notably, all three series support voice/audio sensing for activation and control, with the E83 and E84 MCUs offering enhanced functionality for advanced HMI implementations, such as ML-based wake-up capabilities, vision-based position detection, and face/object recognition. Elevating the feature set further, the PSOC Edge E84 series integrates a low-power graphics display, capable of resolutions up to 1028×768, to meet the demands of diverse application scenarios.

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Filed Under: Aerospace and Defense, Applications, Machine learning, microcontroller

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