Silicon Labs Brings AI and Machine Learning to the Edge with Matter-Ready Platform

By Tiera Oliver

Associate Editor

Embedded Computing Design

January 25, 2022

News

Silicon Labs Brings AI and Machine Learning to the Edge with Matter-Ready Platform

Silicon Labs announced the BG24 and MG24 families of 2.4 GHz wireless SoCs for Bluetooth and Multiple-protocol operations, respectively, and a new software toolkit. 

This new co-optimized hardware and software platform will help bring AI/ML applications and wireless high performance to battery-powered edge devices. Matter-ready, the ultra-low-power BG24 and MG24 families support multiple wireless protocols and incorporate PSA Level 3 Secure Vault protection, ideal for diverse smart home, medical, and industrial applications.

The SoC and software solution for the Internet of Things (IoT) announced today includes:

  • Two new families 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 build and deploy AI and machine learning algorithms using some of the most popular tool suites like TensorFlow.

The single-die BG24 and MG24 SoCs combine a 78 MHz ARM Cortex-M33 processor, high-performance 2.4 GHz radio,  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 a range of 2.4 GHz wireless IoT protocols, these SoCs incorporate security with the RF performance/energy-efficiency.

Today, those considering deploying AI or machine learning at the edge are faced with penalties in performance and energy use that may outweigh the benefits. Per the company, the BG24 and MG24 alleviate those penalties as the first ultra-low powered devices with dedicated AI/ML accelerators built-in. This specialized hardware is designed to handle complex calculations, with internal testing showing up to a 4x improvement in performance along with up to 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 BG24 and MG24 families also have the largest Flash and random access memory (RAM) capacities 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 VaultTM, 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.   

In addition to natively supporting TensorFlow, Silicon Labs has partnered with AI and ML tools providers, like 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. Using this new AI/ML toolchain with Silicon Labs’s Simplicity Studio and the BG24 and MG24 families of SoCs, developers can create applications that draw information from various connected devices, all communicating with each other using Matter to then make intelligent machine learning-driven decisions. 

According to the company, more than 40 companies representing various industries and applications have already begun developing and testing this new platform solution in a closed Alpha program.

EFR32BG24 and EFR32MG24 SoCs in 5 mm x 5 mm QFN40 and 6 mm x 6 mm QFN48 packages are shipping now to Alpha customers and will be available for mass deployment in April 2022. Multiple evaluation boards are available to designers developing applications. Modules based on the BG24 and MG24 SoCs will be available in the second half of 2022. 

To learn more about the new BG24 family, go to: http://silabs.com/bg24.
To learn more about the new MG24 family, go to: http://silabs.com/mg24.
To learn more about how Silicon Labs supports AI and ML, go to: http://silabs.com/ai-ml

Tiera Oliver, Associate Editor for Embedded Computing Design, is responsible for web content edits, product news, and constructing stories. She also assists with newsletter updates as well as contributing and editing content for ECD podcasts and the ECD YouTube channel. Before working at ECD, Tiera graduated from Northern Arizona University where she received her B.S. in journalism and political science and worked as a news reporter for the university’s student led newspaper, The Lumberjack.

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