出版物

TVM 是作为 CPU、GPU 和机器学习加速器的机器学习编译器框架的同行评审研究的一部分而开发的。

This document includes references to publications describing the research, results, and design that use or built on top of TVM.

2018

• TVM: An Automated End-to-End Optimizing Compiler for Deep Learning, [Slides]

• Learning to Optimize Tensor Programs, [Slides]

2020

• Ansor: Generating High-Performance Tensor Programs for Deep Learning, [Slides] [Tutorial]

2021

• Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference, [Slides]

• Cortex: A Compiler for Recursive Deep Learning Models, [Slides]

• UNIT: Unifying Tensorized Instruction Compilation, [Slides]

• Lorien: Efficient Deep Learning Workloads Delivery, [Slides]

• Bring Your Own Codegen to Deep Learning Compiler, [Slides] [Tutorial]

2022

• DietCode: Automatic optimization for dynamic tensor program, [Slides]

• Bolt: Bridging the Gap between Auto-tuners and Hardware-native Performance, [Slides]

• The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding, [Slides]