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Powerful MCUs For Smart, Multi-Use AI

The microcontroller series adds AI to devices, working at different power levels to improve cameras, sensors, and appliances while keeping them efficient.

Synaptics Extends Edge AI Portfolio with High-Performance Adaptive MCUs for Multimodal Context-Aware Computing
Synaptics Extends Edge AI Portfolio with High-Performance Adaptive MCUs for Multimodal Context-Aware Computing

Synaptics has expanded its Synaptics Astra AI-Native platform with the SR-Series adaptive microcontroller units (MCUs) for scalable Edge AI. The series offers three operation tiers: performance (100 GOPS), efficiency, and ultra-low-power always-on (AON) processing. Built on an Arm Cortex-M55 core and Arm Ethos-U55 neural processing unit (NPU), the SR-Series is supported by the Astra Machina Micro development kit and an open-source SDK. It is designed for consumer, enterprise, and industrial IoT applications, featuring accelerators, adaptive vision, and audio and voice algorithms. The compact MCUs include multiple camera interfaces and a range of peripherals to reduce system cost, power, and footprint, making them suitable for devices like battery-powered security cameras, sensors, appliances, point-of-sale systems, digital signage, and scanners.

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“We believe we are at an inflexion point in Edge AI where embedded developers have a unique opportunity to redefine human-machine interaction through multimodal processing and contextual awareness,” said Vikram Gupta, Senior Vice President and General Manager of IoT Processors, Chief Product Officer at Synaptics. “Unlocking this potential requires a new class of embedded compute silicon. As part of our Astra family, the SR-Series extends our Edge AI processing roadmap with intelligence optimized for various power levels. It allows the development of cognitive IoT devices that seamlessly adapt to their surroundings, from ultra-low-power always-on sensing to high-performance edge inference.”

Some of the key features of the SR-Series adaptive MCU include:

  • Up to 4 MB of system memory, including ULP AON memory
  • Streaming vision and audio processing
  • MIPI-CSI camera input and passthrough
  • Low-power image signal processing
  • Secure OTP, TRNG, AES-256, RSA-4096, SHA-512

“Enabling ultra-low-power AI processing at the edge will revolutionize emerging applications across various markets, including retail and smart home, where we are seeing greater performance demands,” said Paul Williamson, Senior Vice President and General Manager of IoT Line of Business at Arm. “With the new SR-Series, built on the Arm compute platform, Synaptics is delivering the real-time intelligence and innovation needed to scale edge AI deployments.” 

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Nidhi Agarwal
Nidhi Agarwal
Nidhi Agarwal is a Senior Technology Journalist at Electronics For You, specialising in embedded systems, development boards, and IoT cloud solutions. With a Master’s degree in Signal Processing, she combines strong technical knowledge with hands-on industry experience to deliver clear, insightful, and application-focused content. Nidhi began her career in engineering roles, working as a Product Engineer at Makerdemy, where she gained practical exposure to IoT systems, development platforms, and real-world implementation challenges. She has also worked as an IoT intern and robotics developer, building a solid foundation in hardware-software integration and emerging technologies. Before transitioning fully into technology journalism, she spent several years in academia as an Assistant Professor and Lecturer, teaching electronics and related subjects. This background reflects in her writing, which is structured, easy to understand, and highly educational for both students and professionals. At Electronics For You, Nidhi covers a wide range of topics including embedded development, cloud-connected devices, and next-generation electronics platforms. Her work focuses on simplifying complex technologies while maintaining technical accuracy, helping engineers, developers, and learners stay updated in a rapidly evolving ecosystem.

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