AI is rapidly shifting from cloud-centric to highly distributed, resource-constrained edge compute devices for embedded and IoT applications. This shift requires a fundamental rethink in AI algorithms, systems design, energy efficiency, and trust. In this keynote, I will explore emerging architectures, algorithms, and hardware–software co-design strategies that enable energy-efficient intelligence at the edge while preserving dependability and explainability. Drawing on recent advances made in these aspects using neurologically-inspired frameworks, such as Tsetlin machines, I will demonstrate how we can achieve high performance with drastically reduced computational cost and energy. I will then conclude by sharing my entrepreneurial journey of empowering industrial edge AI technology using logic based machine learning methods.
