Edge AI – from server rack to handheld
A few years ago, running capable AI inference required a server rack and a cloud subscription. Today, it fits in your hand – and runs on battery. We recently completed hardware verification of a custom Edge AI solution, meeting the customer’s demanding performance requirements.
The hardware
The system is built around a custom PCB designed by Shortlink, with an Nvidia Jetson Nano as the AI accelerator. The board integrates multiple external interfaces and receives real-time image data from a second custom PCB – also designed by us – connected via Power over Coax.
The result: real-time image analysis at the edge, with no cloud dependency and full control over latency and data flow.
Our colleague Tomas Lundström holding the Edge AI module.
Why Edge AI?
The cost of capable AI inference hardware has dropped significantly, making edge deployment increasingly viable in embedded product development. Running inference locally means deterministic latency, no bandwidth dependency, and full data sovereignty.
We’re handling a growing number of Edge AI requests – and it’s an area we find technically rewarding. The hardware constraints are shrinking fast. The real challenge is increasingly in the architecture and application layer.
Let’s build something together
If you’re developing a product that requires local AI inference, feel free to reach out and discuss the technical requirements with us.
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