Deep Learning Compilers

Authors

• Raffi Khatchadourian, CUNY Hunter CollegeFollow

Document Type

Lecture or Presentation

Publication Date

5-12-2026

Abstract

These lecture slides introduce deep learning compilers for a graduate compiler-construction course (CSc 81010). Building on the classical compiler pipeline, they show how modern machine-learning systems compile tensor programs: static tensor and type analysis (illustrated by a WALA/Ariadne-based refactoring of imperative TensorFlow code to graph mode), MLIR-based end-to-end compilation with IREE, and the PyTorch 2.x stack—TorchDynamo graph capture, AOTAutograd, PrimTorch operator decomposition, and TorchInductor lowering to Triton (GPU) and C++/OpenMP (CPU). The slides are a self-contained HTML (W3C Slidy) deck with editable Pandoc Markdown source. Part of a two-session unit on advanced compiler topics; see also "LLMs in Compiler Construction."

Comments

Editable source, build instructions, and license: https://github.com/CSc-81010-Spring-2026/DL-Compilers

Creative Commons License

This work is licensed under a Creative Commons Attribution-Share Alike 4.0 License.