The xG24 family of 2.4 GHz wireless SoCs and a new software toolkit help bring AI/ML applications and wireless high performance to battery-powered edge devices. Matter-ready, the ultra-low-power xG24 family supports multiple wireless protocols and provides PSA Certification Level 3-security , ideal for diverse smart home, medical and industrial applications.
The SoC and software solution for the Internet of Things (IoT) includes:
• A new xG24 family of 2.4 GHz wireless SoCs which feature the industry’s first integrated AI/ML accelerators, support for Matter, Zigbee, OpenThread, Bluetooth Low Energy, Bluetooth mesh, proprietary and multi-protocol operation, the highest level of industry security certification, ultra-low power capabilities and the largest memory and flash capacity in the Silicon Labs portfolio.
• A new software toolkit designed to allow developers to quickly build and deploy AI and machine learning algorithms using some of the most popular tool suites like TensorFlow.
“The xG24 family are our most capable wireless SoCs. The MG24 is the optimal SoC for Matter-enabled high-performance devices in the smart home and other 
environments, while the BG24 brings high accuracy, low current consumption, and the highest PSA Level 3 security ideal for medical and industrial sensor applications,” said Matt Johnson, President, and CEO of Silicon Labs. “With the industry’s first integrated accelerator, we’re bringing the on-device AI/ML
algorithm processing required for advanced IoT applications.”
First Integrated AI/ML Acceleration Improves Performance and Energy Efficiency IoT product designers see the tremendous potential of AI and machine learning to bring even greater intelligence to edge applications like home security systems, wearable medical monitors, sensors monitoring commercial facilities and industrial equipment, and more. But today those considering deploying AI or machine learning at the edge are faced with steep penalties in performance and energy use that may outweigh the benefits.
The xG24 alleviates those penalties as the first ultra-low powered device with the dedicated AI/ML accelerator built in. This specialized hardware is designed to handle complex calculations quickly and efficiently, with internal testing showing a 4x improvement in performance along with a 6x improvement in energy efficiency. Because the ML calculations are happening on the local device rather than in the cloud, network latency is eliminated for faster decision making
and actions.
The xG24 family also has the largest Flash and random access memory (RAM) capacity in the Silicon Labs portfolio. This means that the device can evolve for multi-protocol support, Matter, and trained ML algorithms for large datasets. PSA Level 3 -Certified Secure Vault TM , the highest level of security certification for IoT devices, provides the security needed in products like door locks, medical equipment, and other sensitive deployments where hardening the device from external threats is paramount.
To help users get the most out of the new hardware SoCs, Silicon Labs is also releasing a new AI/ML software toolkit, available now as a pre-Alpha package on GitHub. Silicon Labs partnered with some of the leading providers of open-source AI and ML algorithms, like TensorFlow, SensiML, and Edge Impulse, to ensure that developers have an end-to-end toolchain that simplifies the development of machine learning models optimized for embedded deployments of wireless applications. By using this new AI/ML Toolkit with Silicon Lab’s Unify SDK, released in September 2021, and the xG24 family of SoCs, developers can create
applications that draw information from a variety of connected devices all communicating with each other using Matter to then make intelligent machine learning-driven decisions.
For example, in a commercial office building, many lights are controlled by motion detectors that monitor occupancy to determine if the lights should be on or off. However, when typing at a desk with motion limited to hands and fingers, workers may be left in the dark when motion sensors alone cannot recognize their presence. By connecting audio sensors with motion detectors through the Matter application layer, the additional audio data, such as the sound of typing, can be run through a machine-learning algorithm to allow the lighting system to make a more informed decision about whether the lights should be on or off.
ML computing at the edge enables a variety of other intelligent industrial and home applications, including sensor-data processing for anomaly detection, predictive maintenance, audio pattern recognition for improved glass-break detection, simple-command word recognition, and vision use cases like presence detection or people counting with low-resolution cameras.
The single-die xG24 SoCs combine a 78-MHz ARM Cortex-M33 processor, high-performance 2.4-GHz radio, industry-leading 20-bit ADC, an optimized combination of Flash (up to 1536 kB) and RAM (up to 256 kB) and an AI/ML hardware accelerator for processing machine learning algorithms while offloading the ARM Cortex-M33, so applications have more cycles to do other work. Supporting the broadest range of 2.4 GHz wireless IoT protocols, these SoCs incorporate the highest security with the best RF performance/energy-efficiency ratio in the market.
Silicon Labs, 400 West Cesar Chavez, Austin, TX 78701, 512-416-8500, www.silabs.com
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