Professor Rishad Shafik

Director, Microsystem AI Lab, Co-Founder, Literal Labs, School of Engineering, Newcastle University, UK

Talk Title & Abstract Empowering Energy-efficient and Dependable AI at the Edge AI is rapidly shifting from cloud-centric to highly distributed, resource-constrained edge compute devices. 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 neurosymbolically-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. Biography Rishad Shafik is a Professor of Microelectronic Systems at Newcastle University and an international leader of hardware/software co-design applied in machine learning systems. His research focuses on design methods, circuits, architectures and algorithms for sustainable and explainable machine learning systems. He has led major collaborative projects with academia and industry, published extensively in leading journals and conferences, and delivered invited talks and keynotes internationally. He currently directs the Microsystems AI Lab and is a co-founder of Literal Labs UK.

Talk Title & Abstract

Empowering Energy-efficient and Dependable AI at the Edge

AI is rapidly shifting from cloud-centric to highly distributed, resource-constrained edge compute devices. 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 neurosymbolically-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.

Biography

Rishad Shafik is a Professor of Microelectronic Systems at Newcastle University and an international leader of hardware/software co-design applied in machine learning systems. His research focuses on design methods, circuits, architectures and algorithms for sustainable and explainable machine learning systems. He has led major collaborative projects with academia and industry, published extensively in leading journals and conferences, and delivered invited talks and keynotes internationally. He currently directs the Microsystems AI Lab and is a co-founder of Literal Labs UK.