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Artificial intelligence (AI) provides versatile capabilities in applications such as image classification and voice recognition that are most useful in edge or mobile computing settings. Shrinking these sophisticated algorithms into small form factors with minimal computing resources and power budgets requires innovation at several layers of abstraction: software, algorithmic, architectural, circuit, and device-level innovations. However, improvements to system efficiency may impact robustness and vice-versa. Therefore, a co-design framework is often necessary to customize a system for its given application. A system that prioritizes efficiency might use circuit-level innovations that introduce process variations or signal noise into the system, which may use software-level redundancy in order to compensate. In this tutorial, we will first examine various methods of improving efficiency and robustness in edge AI and their tradeoffs at each level of abstraction.Then, we will outline co-design techniques for designing efficient and robust edge AI systems, using federated learning as a specific example to illustrate the effectiveness of co-design.