AI-Enabled Wireless Chip by Synaptics for Edge IoT
Published on April 24, 2026
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Introduction : An AI enabled wireless system on chip integrates connectivity, sensing and local intelligence into a single semiconductor platform. Recently Synaptics has developed wireless chip model SYN765x with following features.
AI enabled Wireless Chip
The SYN765x combines multiple wireless protocols and embedded AI processing in one device, enabling smart edge devices to perform sensing and control locally.
Key features
- Multi-protcol : Wi-Fi 7, Bluetooth Low Energy (BLE) 6.0, Thread/Zigbee
- On-chip AI processing : Embedded neural processing capability for ML (machine learning) tasks, supports local inference without cloud connectivity.
- Flexible architecture :
- Can work as co-processor with host CPU or MCU
- Can also operate in host less mode, reducing system cost and complexity
- Wireless Sensing capability :
- Uses Wi-Fi Channel State Information (CSI) and BLE Channel Sounding
- Enables presence detection, proximity sensing and motion tracking
- Reduced System Footprint :
- Integration of connectivity, sensing and AI reduces external components.
The integrated capabilities offered by Synaptics AI wireless chip enables edge intelligence which allows devices to process signals locally instead of sending data to cloud servers.
Applications
Following are some of the applications or use cases of AI wireless chip.
- Smart home automation systems
- Intelligent appliances
- Industrial IoT gateways
- Smart security systems
- Human presence sensing devices
- Asset tracking systems
Other manufacturers
| Companies | Products/Features |
|---|---|
| NXP Semiconductors | i.MX 93W processors with integrated NPU and wireless radios |
| Qualcomm | AI-enabled Wi-Fi SoCs for edge devices |
| MediaTek | AIoT Chipsets combining connectivity and edge AI |
| Espressif Systems | ESP32 series with AI acceleration features |
| Nordic Semiconductor | Wireless SoCs with ML support for IoT devices |
Conclusion:
AI-enabled wireless chips from Synaptics integrate connectivity, sensing and machine learning into a single platform which enables smarter edge devices. This integration reduces system complexity while enabling real time intelligence in IoT and automation systems.
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