Compilers for Machine Learning
Machine learning applications are becoming ubiquitous in large-scale production systems. With that growth and the scaling in data volume and model complexity, the focus on efficiently executing machine learning models has become even greater. The push for increased energy efficiency has led to the emergence of diverse heterogeneous system and accelerator architectures. In parallel, model complexity and diversity pushed for higher productivity systems, more powerful programming abstractions, type systems, language embeddings, frameworks and libraries. Compilers have historically been the bridge between programmer efficiency and high performance code, allowing the expression of code that remains understandable and productive to port and extend, while producing high-performance code for diverse architectures. As such, compiler techniques have been increasingly incorporated into machine learning frameworks. This goes both ways: given the broadening gap between high-level constructs and hardware accelerators, compilers in machine learning frameworks also emerged as natural clients of machine learning techniques, from domain-specific heuristics to autotuning.
This workshop aims to highlight cutting edge work and research that incorporate compiler techniques and algorithms in optimizing machine learning workloads. Compiler techniques affect a large part of the machine learning stack. The workshop topics span from high-level abstract representations to code generation for accelerators. The list of invited speakers are similarly experts across the different levels of the stack. The workshop does not have formal proceedings, and presentations will include ample time for interaction.
- Diego Caballero, Intel
- Albert Cohen, Google
- Jacques Pienaar, Google
- Tatiana Shpeisman, Google
- Ayal Zaks, Intel and Technion