Analysis of Synaptics SR Series MCUs: Performance for Edge AI
Synaptics has launched its SR series of high-performance adaptive microcontrollers (MCUs) in Nuremberg, Germany, expanding its Astra AI-Native platform, targeting multi-modal contextual computing needs in edge AI and Internet of Things (IoT).
The SR series, based on Arm Cortex-M55 and Ethos-U55 NPU, offers three power levels (100 GOPS performance, efficiency, and ultra-low power AON), combined with an open-source SDK and Machina Micro suite, optimizing vision, audio, and voice processing, supporting low-power cognitive devices from smart homes to industrial applications.
The SR series fills the gap in performance and energy efficiency of traditional MCUs under AI workloads with an innovative three-tier architecture, driving the scalability and standardization of edge AI.
We analyze the breakthroughs and impact of the SR series from two aspects: technical architecture and innovation, application potential and market competition, and look ahead to its prospects in the IoT ecosystem.
Part 1: Synaptics SR Technical Architecture and Innovation
The launch of Synaptics SR series MCUs marks a significant advancement in edge AI chip design, with its core focusing on providing a “three-gear” architecture solution for multi-modal contextual awareness needs in IoT devices.
The three chips—SR110, SR105, and SR102—are positioned for high performance (100 GOPS), efficiency, and ultra-low power always-on (AON) applications, respectively. All adopt the Arm Cortex-M55 core (up to 400MHz, with Helium technology), with SR110 and SR105 integrating the Ethos-U55 NPU, and SR110 additionally equipped with a Cortex-M4.
This layered design breaks the limitations of traditional MCUs’ single power mode, enabling seamless transition from ultra-low power monitoring to high-performance AI inference through dynamic switching of computing domains.
Compared to existing 32-bit MCU rigid architectures, the SR series shows significant improvements in performance (100 GOPS vs. traditional 10-20 GOPS), energy efficiency (microwatt-level AON vs. milliwatt-level standby), and flexibility, filling the hardware gap for AI-ready IoT devices.
- Technical highlights are reflected in multi-level computing and hardware optimization.
◎ The ultra-low power AON domain continuously monitors the environment at microwatt-level power consumption, supporting visual (MIPI-CSI camera input) and audio event detection, with built-in low-power pre-roll functionality to store data before and after triggering, suitable for battery-powered devices such as security cameras (standby power <100μW).
◎ The efficiency domain combines Cortex-M4 and a micro NPU (10 GOPS), running lightweight AI models (such as object detection, with power consumption around 50mW), meeting real-time processing requirements.
Up to 4MB of system memory (including ULP AON memory), streaming ISP, and rich peripherals (USB, serial ports, secure OTP) further reduce system costs and integration difficulties, with BOM costs expected to be 20%-30% lower than traditional solutions.
- The software ecosystem is another major innovation of the SR series.
◎ Astra Machina Micro suite and open-source SDK (supporting FreeRTOS, Zephyr) provide an “out of the box” experience, compatible with existing IoT ecosystems, lowering development barriers.
◎ Compared to competitors’ proprietary toolchains (such as NXP’s MCUXpresso), the openness of the SR series shortens the cycle from prototype to mass production (estimated 6-9 months vs. 12-18 months).
◎ Security features (AES-256, RSA-4096, SHA-512) ensure data protection for edge devices in industrial and consumer scenarios, meeting regulatory requirements such as GDPR.
This collaborative design of hardware and software not only solves the energy efficiency bottleneck of traditional MCUs under AI workloads but also provides a standardized platform for multi-modal processing (such as vision + voice fusion), promoting the large-scale deployment of edge AI.
Part 2: Application Potential and Market Competition of IoT Edge AI Chips
- The application potential of the SR series covers consumer, enterprise, and industrial IoT, filling the gap for low-power cognitive devices.
The AON feature of SR102 is suitable for smart locks or voice assistants, always listening for voice commands (power consumption <50μW), processing simple inference (such as keyword detection) after waking up.
◎ In the industrial field, SR105 can be used for sensor nodes, combining vision and vibration data to detect equipment failures (10 GOPS inference, power consumption <100mW), extending the life of unattended devices.
◎ In enterprise scenarios, SR110 supports POS terminals or digital signage, implementing multi-modal interaction (gesture + voice, power consumption <1W), enhancing user experience while reducing system power consumption.
This multi-scenario adaptability benefits from the scalability of the SR series, making it competitive in devices with power budgets ranging from microwatts to watts.
- In market competition, the SR series directly faces traditional MCU giants such as NXP, ST, and TI, as well as edge AI newcomers like Ambiq and GreenWaves.
◎ NXP’s i.MX RT1170 (Cortex-M7, 1GHz) provides high performance (>1GOPS), but higher power consumption (standby >1mW, inference >500mW), lacks AON design, and struggles to meet ultra-low power requirements.
◎ ST’s STM32H7 (Cortex-M7, 480MHz) integrates lightweight AI acceleration (<5 GOPS), but doesn’t support complex inference (TOPS efficiency <0.1W/TOP), positioned more for basic applications.
◎ TI’s CC13x2 (Cortex-M4, 48MHz) focuses on low power (standby <10μW), but has no NPU, limiting AI capabilities (<1 GOPS).
In comparison, the SR series’ three-tier architecture balances performance (100 GOPS vs. 5-10 GOPS) and energy efficiency (standby <100μW vs. 1mW), occupying a differentiated advantage in the IoT AI market.
◎ Newcomers like Ambiq’s Apollo4 (Cortex-M4, 96MHz) emphasize ultra-low power (standby <5μW), but inference capability is only about 0.5 GOPS, making it difficult to support multi-modal tasks;
◎ GreenWaves’ GAP9 (RISC-V, 10 GOPS) has excellent energy efficiency (<50mW), but lacks ecosystem support.
The SR series forms comprehensive competitiveness in performance-energy efficiency-development with the Arm ecosystem, open-source SDK, and 100 GOPS peak performance.
Although Arm Cortex-M55 and Ethos-U55 are mature, the absence of sub-3nm processes (such as TSMC 5nm) limits their power optimization (compared to Apollo4’s 22nm), possibly making them slightly inferior in extremely low-power scenarios.
Market promotion relies on Synaptics’ channel capabilities; if unable to quickly penetrate smart home and industrial customers (expected shipments <5 million units in 2025), it will face pressure from TI and NXP.
While the open-source ecosystem lowers barriers, deep integration with mainstream AI frameworks such as TensorFlow Lite still needs improvement, otherwise developers may turn to more mature ecosystems (such as NVIDIA Jetson).
In the future, if the SR series can upgrade its process (e.g., 7nm) and expand its ecosystem (e.g., support for ONNX), it will occupy a larger share of the edge AI market, with revenue expected to contribute over 30% of Synaptics’ IoT business by 2026.
Summary
Synaptics SR series MCUs set a new benchmark for the integration of edge AI and IoT with their innovative three-tier architecture and open-source ecosystem. The 100 GOPS performance, microwatt-level AON power consumption, and multi-modal processing capabilities solve the energy efficiency and flexibility pain points of traditional MCUs under AI workloads, applicable to a wide range of scenarios from smart homes to industrial automation.
Facing competition from giants such as NXP and ST, the SR series gains an early advantage with the Arm platform and development convenience, but process technology and ecosystem improvement remain key to its breakthrough.
With 2025 being a turning point for edge AI, the launch of the SR series not only strengthens Synaptics’ position in the IoT chip market but also signals the beginning of a scaling wave in this field.
