STMicroelectronics extends STM32Cube.AI development tool

Now, with support for deep quantisation input formats like qKeras or Larq, developers can even further reduce network size, memory footprint, and latency.

These benefits unleash more possibilities from AI at the edge, including frugal and cost-sensitive applications.

Developers can thus create edge devices, such as self-powered IoT endpoints that deliver advanced functionality and performance with longer battery runtime.

ST’s STM32 family provides many suitable hardware platforms.

The portfolio extends from ultra-low-power Arm Cortex-M0 MCUs to high-performing devices leveraging Cortex-M7, -M33, and Cortex-A7 cores.