Inside TensorFlow - MLIR for TF Developers

Dive into a technical deep dive on MLIR (Multi-Level Intermediate Representation) for TensorFlow developers in this 44-minute video presentation by Software Engineer Jacques Pienaar. Explore the concept of MLIR, its importance, and how it enhances reusability in TensorFlow. Learn about new abstractions, progressive lowering, and the goals behind MLIR implementation. Discover the MLIR infrastructure, its impact on the TF-Lite converter, and improvements in usability and error debugging. Gain insights into the Newton's flow compiler, reusable compiler passes, and the Radio6 example. Understand the current approach to MLIR, including MLIR Opt, MLIR Translate, MLIR Locations, and MLIR Legalization. Delve into verification processes and memory safety considerations. Enhance your understanding of TensorFlow's internal workings and compiler infrastructure through this in-depth presentation.