.. Licensed to the Apache Software Foundation (ASF) under one or more contributor license agreements. See the NOTICE file distributed with this work for additional information regarding copyright ownership. The ASF licenses this file to you under the Apache License, Version 2.0 (the "License"); you may not use this file except in compliance with the License. You may obtain a copy of the License at .. http://www.apache.org/licenses/LICENSE-2.0 .. Unless required by applicable law or agreed to in writing, software distributed under the License is distributed on an "AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY KIND, either express or implied. See the License for the specific language governing permissions and limitations under the License. Publications ============ TVM is developed as part of peer-reviewed research in machine learning compiler framework for CPUs, GPUs, and machine learning accelerators. 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_] .. __: https://arxiv.org/abs/1802.04799 .. _Slides: https://www.usenix.org/system/files/osdi18-chen.pdf * `Learning to Optimize Tensor Programs`__, [Slides] .. __: https://arxiv.org/pdf/1805.08166.pdf 2020 * `Ansor: Generating High-Performance Tensor Programs for Deep Learning`__, [Slides__] [Tutorial__] .. __: https://arxiv.org/abs/2006.06762 .. __: https://www.usenix.org/sites/default/files/conference/protected-files/osdi20_slides_zheng.pdf .. __: https://tvm.apache.org/2021/03/03/intro-auto-scheduler 2021 * `Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference`__, [Slides__] .. __: https://arxiv.org/abs/2006.03031 .. __: https://shenhaichen.com/slides/nimble_mlsys.pdf * `Cortex: A Compiler for Recursive Deep Learning Models`__, [Slides__] .. __: https://arxiv.org/pdf/2011.01383.pdf .. __: https://mlsys.org/media/mlsys-2021/Slides/1507.pdf * `UNIT: Unifying Tensorized Instruction Compilation`__, [Slides] .. __: https://arxiv.org/abs/2101.08458 * `Lorien: Efficient Deep Learning Workloads Delivery`__, [Slides] .. __: https://assets.amazon.science/c2/46/2481c9064a8bbaebcf389dd5ad75/lorien-efficient-deep-learning-workloads-delivery.pdf * `Bring Your Own Codegen to Deep Learning Compiler`__, [Slides] [Tutorial__] .. __: https://arxiv.org/abs/2105.03215 .. __: https://tvm.apache.org/2020/07/15/how-to-bring-your-own-codegen-to-tvm 2022 * `DietCode: Automatic optimization for dynamic tensor program`__, [Slides] .. __: https://proceedings.mlsys.org/paper/2022/file/fa7cdfad1a5aaf8370ebeda47a1ff1c3-Paper.pdf * `Bolt: Bridging the Gap between Auto-tuners and Hardware-native Performance`__, [Slides] .. __: https://proceedings.mlsys.org/paper/2022/file/38b3eff8baf56627478ec76a704e9b52-Paper.pdf * `The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding`__, [Slides] .. __: https://arxiv.org/abs/2110.10221