Future of Compute by Jim Keller

Date : 27 April 2022
Designing a high performance architecture to run continually evolving machine learning models is a complex problem. At the same time, rapid adoption of AI is increasing the need for scalable systems to run such models. This technical talk will apply first-principles thinking to system architecture that can be the foundation for AI/ML compute. We will discuss the structure of a machine learning model and how next generation AI compute uses Scalar, Vector, Matrix and Tensor architecture. We will also expand on the components that will become the backbone of AI architectures, such as new data formats and RISCV - an open source instruction set architecture. Finally we will look at the implications of building chips and systems in this new era of artificial intelligence and how it will drive Foundry, Silicon, SOC, IPs, Data Center, Cloud and Software 2.0, the next iteration in software development. The talk will be followed by a Networking and Interaction Session with the Students.