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Awesome Tensor Compilers

by Community

A list of awesome compiler projects and papers for tensor computation and deep learning.

AT

OSS

Awesome Tensor Compilers

Added 1 June 2026

#code-generation #compiler #deep-learning #high-performance-computing #machine-learning #programming-language #tensor

Overview

A curated GitHub repository listing compiler projects and research papers for tensor computation and deep learning. Maintained by the community, it serves as a reference for developers and researchers interested in tensor compiler technology.

Best for

Best for
Researchers and engineers exploring tensor compilation and deep learning optimization.

Use cases

  • Discovering tensor compiler projects for model optimization
  • Finding research papers on tensor compilation techniques
  • Identifying tools for deep learning hardware acceleration

Notes

A curated GitHub repository listing compiler projects and research papers for tensor computation and deep learning. Maintained by the community, it serves as a reference for developers and researchers interested in tensor compiler technology.

2,753 stars on GitHub. Last updated 2024-10-19.

Use cases

  • Discovering tensor compiler projects for model optimization
  • Finding research papers on tensor compilation techniques
  • Identifying tools for deep learning hardware acceleration

Pros

  • Comprehensive collection of projects and papers across the field
  • Community-maintained with regular updates
  • Covers both academic research and industry tools

Cons

  • No hands-on guidance or usage instructions for individual tools
  • List format requires manual exploration and evaluation
  • May not include detailed comparisons or benchmarks

Indexed from awesome-llmops and enriched against its public facts.

Pros

  • Comprehensive collection of projects and papers across the field
  • Community-maintained with regular updates
  • Covers both academic research and industry tools

Cons

  • No hands-on guidance or usage instructions for individual tools
  • List format requires manual exploration and evaluation
  • May not include detailed comparisons or benchmarks
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