In the world of embedded devices, efficient and low-powered devices with uncompromised connectivity are crucial to keep all of our devices working properly. With the growing number of connected devices in our homes (i.e. home appliances), Qualcomm’s new solutions seek to improve that experience.
At Embedded World 2024, Qualcomm announces new breakthroughs – the QCC730 WiFi solution and RB3 Gen 2 platform.
Qualcomm QCC730
The Qualcomm QCC730 WiFi is the company’s latest micro-powered WiFi system made specifically for IoT devices. It uses 88% less power than the previous generations and makes battery-operated WiFi-connected devices actually feasible.
This also enables IoT devices to use WiFi instead of Bluetooth for more flexible connectivity and direct access to the cloud without needing a hub.
The QCC730 WiFi is using a 1×1 band on the 802.11a/b/g/n WiFi protocol – that means WiFi-4 networks. While it might seem like it’s many generations behind since we’re on WiFi-7 now, WiFi-4 is actually one of the best in terms of connection strength and reliability. IoT devices don’t need high bandwidth, so WiFi-4 is perfect.
Qualcomm also announced the QCC711, a tri-core ultra-low power Bluetooth Low Energy SoC and QCC740, an all-in-one solution supporting Thread, Zigbee, WiFi, and Bluetooth.
Qualcomm RB3 Gen 2 platform
Introducing a new platform that supports a multitude of functions, made for automation. It offers comprehensive hardware and software solutions by utilizing the Qualcomm QCS6490 processor and provide 10x increase in on-device AI processing.
This is important as it also supports 4x8MP+ camera sensors for computer vision – so we can embed it into robots, drones, and other industrial devices.
This platform is now available for pre-order in two integrated development kits and supports downloadable software updates to simplify application development, integration, and build proof-of-concepts and prototypes.
The RB3 Gen 2 is also supported in the recently announced Qualcomm AI Hub, which contains a library of continuously refreshed pre-optimized AI models for superior on-device AI performance, lower memory utilization, and power optimized operation.
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