Seeing, understanding, and responding: low-power CNN, VLM and SLM workloads on Raspberry Pi 5

A 3W NPU from Sixfab and DEEPX expands the possibilities for vision and compact language AI at the edge. Seeing, understanding, and responding: low-power CNN, VLM and SLM workloads on Raspberry Pi 5. Most edge AI/ML workloads belong on the CPU, but accelerators are important for the small minority of workloads that the CPU can’t accommodate.

Raspberry Pi users have a variety of choices for acceleration, with our own AI HAT+ and HAT+ 2 as well as offerings from the wider Raspberry Pi ecosystem. Here, our friends at Sixfab and DEEPX discuss running vision and compact language AI on Raspberry Pi 5 with the Sixfab AI HAT+, and what a three-watt NPU makes possible at the edge. Raspberry Pi has made AI development accessible to millions of engineers, students and makers.

The next step is moving from a model that runs once, in a demo, to an intelligent system that keeps watching, understanding and responding — without depending on a constant cloud connection. The Sixfab AI HAT+ for Raspberry Pi 5 is a third-party AI accelerator board built around the DEEPX DX-M1M NPU. It adds 25 TOPS of dedicated AI acceleration at about 3 W of typical sustained NPU power, while leaving the Raspberry Pi’s CPU free for camera handling, application logic, connectivity and device control.

In this article we’d like to show what that combination is genuinely good at, where it isn’t the right tool, and how to get from first boot to first inference in a few minutes.