This tutorial presents a new end-to-end tensor compiler infrastructure designed to support emerging tensor accelerators.

Recent years have seen a proliferation of specialized ML accelerators - proposed in both academia (e.g., Gemmini, FEATHER) and industry (e.g., Google TPU, Intel AMX) - that depart significantly from traditional CPU/GPU architectures. However, research on compiler and systems support for these accelerators remains sparse, largely due to the lack of open-source compiler infrastructures capable of targeting them from modern ML frameworks like PyTorch, TensorFlow, and JAX. Building such support typically involves considerable manual effort, slowing innovation and creating a gap between hardware and software research communities.

Our infrastructure closes this gap by automatically generating compiler support from a high-level description of an accelerator’s ISA. It enables rapid prototyping and evaluation of architecture-specific optimizations, significantly lowering the gap for systems research on non-traditional architectures.

The tutorial is designed for researchers, practitioners, and students interested in compiler design, programming languages, and AI/ML hardware. By the end, participants will have hands-on experience with the complete ACT workflow and understand how to rapidly prototype compiler support for novel accelerator architectures.

For details on the tutorial timeline, visit https://act-compiler.github.io/tutorials/splash2025/