One Ware implemented a custom CNN model using a small fraction of the MAX 10 logic capacity. Compared to Nvidia's Jetson Nano, this approach offers significant advantages in power consumption, accuracy and cost.
The model requires quantization-aware training and intelligent dimension reduction to fit within the FPGA constraints. The result is a compact inference engine that runs at wire speed directly on the production line.
MAX 10 is widely adopted in factories thanks to its robustness for industrial equipment. Deploying improved detection algorithms on existing hardware minimizes organizational disruption — no new infrastructure, no GPU servers, just a bitstream update.
This is a compelling example of how low-cost FPGAs can deliver edge AI inference at ultra-low latency for real-time quality control.