At Embedded World 2025, Synaptics’ Chief Product Officer, Vikram Gupta, presented the company’s latest developments in context-aware computing for IoT edge applications. Gupta detailed Synaptics’ approach to creating multimodal AI microcontroller units (MCUs), designed to significantly enhance IoT device capabilities by integrating audio and visual sensing. Synaptics also introduced the new SR-Series processors, expanding their Astro family, aimed specifically at balancing performance, power efficiency, and AI capabilities. These devices are targeted at bridging the gap between traditional microcontrollers (MCUs) and high-performance microprocessor units (MPUs). For more information, visit https://www.synaptics.com.
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Synaptics is my Embedded World 2025 video coverage sponsor, check out my Synaptics videos here: https://www.youtube.com/playlist?list=PL7xXqJFxvYvhAbQoe9YN4c84SqXxIY3fQ
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Synaptics highlighted the importance of multimodal sensing in IoT, emphasizing the fusion of audio and visual modalities. This integration allows for advanced features such as presence detection, personalized experiences through facial and voice recognition, and improved context-aware interactions. Devices equipped with these multimodal MCUs can intelligently adjust their behavior based on user identity, attention, or movement within their environment.
Gupta explained that the SR-Series MCUs sit strategically between lower-powered MCUs and more power-intensive MPUs. They utilize a sophisticated three-tier architecture, operating seamlessly between always-on, low-power modes, intermediate efficiency modes, and high-performance states when required. This tiered approach ensures maximum battery efficiency while maintaining robust processing capabilities, making them ideal for applications demanding continuous and contextually responsive operation.
Synaptics also addressed common industry challenges, particularly the current fragmentation and complexity faced by developers implementing edge AI solutions. Gupta emphasized Synaptics’ commitment to open standards and collaboration with open-source communities to drive broader adoption and interoperability. By using standardized frameworks, developers can more efficiently integrate AI models into diverse IoT devices, streamlining development cycles.
Gupta introduced Synaptics’ new “Machina Micro” development kit, a compact platform specifically designed for developers working with the SR-Series MCUs. The Machina Micro facilitates rapid prototyping, enabling engineers to quickly evaluate AI-driven functionalities, optimize system performance, and deploy context-aware features efficiently across various IoT applications, from smart home automation to industrial sensors.
Specific use-case scenarios highlighted by Gupta included smart TVs capable of detecting user presence and adjusting settings accordingly, creating personalized viewing experiences. Other sectors identified as beneficiaries include elderly care applications, where AI-driven multimodal sensing can enhance monitoring and safety, and industrial automation systems that benefit from improved efficiency and predictive maintenance capabilities.
Security was another key aspect discussed, with Gupta outlining how the SR-Series incorporates secure OTP (one-time programmable) memory, true random number generators, and support for advanced cryptographic algorithms such as AES-256 and RSA-4096. These security features are crucial for protecting sensitive user data and ensuring reliable operation in connected environments vulnerable to cyber threats.
Gupta concluded by expressing Synaptics’ commitment to continued innovation and collaboration, inviting further industry partnerships to expand the ecosystem surrounding context-aware IoT edge computing. He emphasized that the SR-Series marks a significant step forward, enabling developers and businesses to create more intelligent, efficient, and secure IoT devices for a wide array of applications.
Check out all my Embedded World 2025 videos in this playlist: https://www.youtube.com/playlist?list=PL7xXqJFxvYvjgUpdNMBkGzEWU6YVxR8Ga
