editor's blog
Subscribe Now

Freescale’s Sensor Hub Integration

We’ve seen the move from APs managing sensors to MCUs acting as sensor hubs to integration of sensors with MCUs, as with ST. Well, Freescale has now jumped in as well, integrating an accelerometer with a 32-bit Coldfire V1 MCU into what they’re calling their Xtrinsic Intelligent Motion platform.

Given the number of IMUs out there integrating accelerometers, gyros, and magnetometers, I asked why they were just going with an accelerometer only. They said that, frankly, in earlier days, customers didn’t seem interested in sensor hubs. They’re coming around, and clearly they’ve made their way into phones. But now industrial and other customers are starting to take note. But they’re not so much interested in the gyros and magnetometers.

That’s not to say you can’t use them; you can add other sensors into the hub via their I2C/SPI connectivity.

One of the other challenges they see for users is the fact that most sensor hub environments are closed, limiting your choice of sensors and software. They’re trying to keep things as open as possible, allowing you to integrate whatever other sensors and software they want from any vendors they choose. Freescale will provide free software, but you’re not locked in.

You can find more information in their release.

Leave a Reply

featured blogs
Nov 15, 2024
Explore the benefits of Delta DFU (device firmware update), its impact on firmware update efficiency, and results from real ota updates in IoT devices....
Nov 13, 2024
Implementing the classic 'hand coming out of bowl' when you can see there's no one under the table is very tempting'¦...

featured video

Introducing FPGAi – Innovations Unlocked by AI-enabled FPGAs

Sponsored by Intel

Altera Innovators Day presentation by Ilya Ganusov showing the advantages of FPGAs for implementing AI-based Systems. See additional videos on AI and other Altera Innovators Day in Altera’s YouTube channel playlists.

Learn more about FPGAs for Artificial Intelligence here

featured paper

Quantized Neural Networks for FPGA Inference

Sponsored by Intel

Implementing a low precision network in FPGA hardware for efficient inferencing provides numerous advantages when it comes to meeting demanding specifications. The increased flexibility allows optimization of throughput, overall power consumption, resource usage, device size, TOPs/watt, and deterministic latency. These are important benefits where scaling and efficiency are inherent requirements of the application.

Click to read more

featured chalk talk

SLM Silicon.da Introduction
Sponsored by Synopsys
In this episode of Chalk Talk, Amelia Dalton and Guy Cortez from Synopsys investigate how Synopsys’ Silicon.da platform can increase engineering productivity and silicon efficiency while providing the tool scalability needed for today’s semiconductor designs. They also walk through the steps involved in a SLM workflow and examine how this open and extensible platform can help you avoid pitfalls in each step of your next IC design.
Dec 6, 2023
59,184 views