Polymage Labs Ties Up with Tenstorrent to Build Compiler for AI Hardware

Polymage Labs a deep-tech startup incubated at the Indian Institute of Science (IISc), has entered into a strategic collaboration with US-based Tenstorrent Inc to develop an AI compiler for Tenstorrent's AI accelerators.

The partnership brings together Polymage Labs' compiler expertise and Tenstorrent's AI computing platforms with the aim of improving software support for advanced AI hardware. Both companies said the collaboration focuses on addressing long-standing challenges in making specialised AI chips easier to program and deploy.

According to Polymage Labs, compiler software plays a critical role in translating high-level instructions written by developers into optimised code that can run efficiently on complex hardware. The company said such software is essential for simplifying the use of next-generation AI accelerators, which often require sophisticated programming approaches.

Within a few months of working together, Polymage Labs' PolyBlocks compiler framework has been integrated with Tenstorrent's hardware platforms. The company said this integration delivers strong performance directly from unmodified, high-level PyTorch and JAX code, two widely used frameworks in machine learning and AI research.

The collaboration also seeks to address a key bottleneck in the AI hardware industry the lack of a mature and seamless software ecosystem, which has slowed the adoption of several accelerator platforms.

"The synergy between the Polymage and Tenstorrent teams has been exceptional" said Uday Bondhugula, Chief Technology Officer of Polymage Labs and Professor of Computer Science at IISc. "Using modern MLIR-based compiler infrastructures in both PolyBlocks and Tenstorrent's tt-mlir allowed us to develop a complete, end-to-end compiler for PyTorch and JAX in just a few months."

PyTorch is an open-source deep learning framework originally developed by Meta Platforms and supported by the Linux Foundation, while JAX is a Python-based library designed for high-performance numerical computing and large-scale machine learning.