Setting the Stage for a Successful AI Project
AI and machine vision projects are no different from other embedded processor projects that need to meet budget, performance, and the operational goals of a program. But the path to that success incorporates decidedly different development workflows. As an example, unlike typical embedded system deployments, which may require occasional hardware or software updates, the updates required for AI inference engines must be regularly scheduled and built into the operational plan to incorporate deep learning results from new (or changing) data. Another common occurrence in AI projects is the underestimation of the amount of data required for successful AI training.
To help set the stage for success, Dowson Robotics (Dowson) and Wolf Advanced Technology (WOLF) have teamed up to share our understanding of the common development workflows used to mitigate risk and to help accommodate resource requirements.
As a matter of background, Dowson has provided technical consulting and management services for a number of successfully deployed applications based on artificial intelligence and computer vision, and WOLF creates GPU and FPGA-based modules that are currently being used in a number of AI and computer vision programs for defense and aerospace.