Specifications
Product Class
AI-native MCU with integrated wireless connectivity
MCU Core
Arm Cortex-M52 at 200 MHz with Arm Helium vector extensions
On-Chip SRAM
1 MB
NPU
Integrated Arm Ethos-U55 at 200 MHz, 50 GOPS
Inference Target
Efficient on-device AI inference at low power
Security
Arm TrustZone, AES, SAU and MPU
Peripherals
SDIO, SPI, UART, I²C, PDM, ADC, DAC, PWM, USB
Wi-Fi
Tri-band 1×1 Wi-Fi 7 (2.4 / 5 / 6 GHz)
Bluetooth
Bluetooth 6.0 (BLE)
802.15.4
Zigbee or Thread radio with full Matter compliance
Positioning
Always-on sensing, contextual awareness, on-device decision-making
Overview
The Synaptics Astra SRW1500 Series is a family of AI-native MCUs that pair an Arm Cortex-M52 processor at 200 MHz with Helium vector extensions and an integrated Arm Ethos-U55 NPU rated at 50 GOPS, alongside 1 MB of on-chip SRAM and a full tri-band 1×1 Wi-Fi 7 radio with Bluetooth 6.0 and IEEE 802.15.4.
The series sits within the broader Synaptics Astra edge AI portfolio, which spans the SL-series high-performance edge processors and the SR-series microcontrollers. The SRW1500 variant is specifically the wireless-integrated member: Zigbee or Thread with full Matter compliance is on the same die as the AI accelerator, so a single part can act as both the compute node and the fabric-connected endpoint.
For system designers the relevant combination is the security and peripheral set: Arm TrustZone plus AES, SAU and MPU for a secure boot and provisioning chain, and SDIO, SPI, UART, I²C, PDM, ADC, DAC, PWM and USB for sensor and actuator interfaces. That makes the part usable as the sole controller in a battery-powered sensing product that also performs local inference.
Key Benefits
50 GOPS Ethos-U55 NPU with a 200 MHz Cortex-M52 host on one die. 1 MB on-chip SRAM holds the inference working set without external memory. Tri-band Wi-Fi 7 + Bluetooth 6.0 + Thread/Zigbee with Matter in a single part. Arm TrustZone, AES, SAU and MPU for secure device provisioning.
Applications
Battery-powered smart sensors and actuators, Matter-based smart-home endpoints, industrial condition-monitoring nodes running local anomaly detection, and IoT products that must classify events locally rather than streaming raw data to the cloud.
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