MLIR: Accelerating AI
MLIR is TensorFlow’s open source machine learning compiler infrastructure that addresses the complexity caused by growing software and hardware fragmentation and makes it easier to build AI applications. Chris Lattner and Tatiana Shpeisman explain how MLIR is solving this growing hardware and software divide and how it impacts you in the future.
Chris Lattner is a distinguished engineer at Google leading the TensorFlow infrastructure and Swift for TensorFlow teams. His work cross-cuts a wide range of compiler, runtime, and other system infrastructure projects for high-performance machine learning accelerators, including CPUs, GPUs, TPUs, and mobile accelerators. Chris is the founder and chief architect of the LLVM and Clang projects and creator of the Swift programming language, and he drives the MLIR project at Google. He also serves on the LLVM Foundation’s board of directors and the Swift core team.
Tatiana Shpeisman is an engineering manager in Google Brain, where she leads the team working on TensorFlow graph compiler, MLIR, and TensorFlow infrastructure for GPUs and CPUs. Previously, she led Intel Labs’s efforts to deliver programmability and performance to modern parallel and heterogeneous computing platforms. Tatiana is passionate about using compiler technology to build better machine learning systems. She holds a PhD in computer science from the University of Maryland, College Park.
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