Machine learning (ML) algorithms are moving to the IoT edge due to various considerations such as latency, power consumption, cost, network bandwidth, reliability, privacy and security. Hence, there ...

Going from a microcontroller blinking an LED, to one that blinks the LED using voice commands based on a data set that you trained on a neural net work is a “now draw the rest of the owl&#822… ...

You wind up with a much more efficient or smaller thing that could be embedded into microcontrollers. Roddy: That’s a whole burgeoning field. Taking a step back, machine learning has really two ...

While embedded processor vendors usually focus on the deployment side of machine learning (ML) designs, NXP has taken the extra step of offering tools for data preparation and model training. NXP, ...

Albert Zhichun Li, Ph.D., is Chief Security Scientist at Stellar Cyber. He has over 15 years of experience in cybersecurity research. Not long ago, the state of voice recognition was quite ...

The leading development toolchain IAR Embedded Workbench supports the neural network library Arm CMSIS-NN. Uppsala, Sweden—February 26, 2018—IAR Systems®, the future-proof supplier of software tools ...

The two new FM4 systems are based on the ARM Cortex-M4. One includes a 2D graphics engine with on-board VRAM while the other includes voice recognition software and hardware support.

Arm’s Project Trillium addresses machine learning (ML) and it includes a number of components, including the new ML processor (Fig. 1). This platform will support the Arm CPU complexes in a ...

Whether it's search, Google Assistant, Android, Gmail, Google Photos, or Google Cloud Platform and its data centers, the path to success for Google flows through two words: Artificial Intelligence ...