Abstractions for Machine Learning Compilations
Abstract:
Deploying deep learning models on various devices has become an important topic. Machine learning compilation is an emerging field that leverages compiler and automatic search techniques to accelerate AI models. ML compilation brings a unique set of challenges: emerging machine learning models; increasing hardware specialization brings a diverse set of acceleration primitives; growing tension between flexibility and performance.
Multiple layers of abstractions and corresponding optimizations are needed to solve these challenges at different levels of a system. In this talk, I will talk about our experiences designing abstractions. I will then discuss the new challenges brought by multiple abstractions themselves and our recent effort to tackle these challenges through unifying representation and ML-driven automation.
Short Bio:
Tianqi Chen is currently an Assistant Professor at the Machine Learning Department and Computer Science Department of Carnegie Mellon University. He is also the Chief Technologist of OctoML. He received his PhD. from the Paul G. Allen School of Computer Science & Engineering at the University of Washington. He has created many major learning systems that are widely adopted: XGBoost, TVM, and MXNet (co-creator).