wehub-resource-sync
85 lines
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ReStructuredText
85 lines
3.0 KiB
ReStructuredText
.. Licensed to the Apache Software Foundation (ASF) under one
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or more contributor license agreements. See the NOTICE file
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distributed with this work for additional information
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regarding copyright ownership. The ASF licenses this file
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to you under the Apache License, Version 2.0 (the
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"License"); you may not use this file except in compliance
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with the License. You may obtain a copy of the License at
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.. http://www.apache.org/licenses/LICENSE-2.0
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.. Unless required by applicable law or agreed to in writing,
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software distributed under the License is distributed on an
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"AS IS" BASIS, WITHOUT WARRANTIES OR CONDITIONS OF ANY
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KIND, either express or implied. See the License for the
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specific language governing permissions and limitations
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under the License.
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Publications
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============
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TVM is developed as part of peer-reviewed research in machine learning compiler
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framework for CPUs, GPUs, and machine learning accelerators.
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This document includes references to publications describing the research,
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results, and design that use or built on top of TVM.
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2018
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* `TVM: An Automated End-to-End Optimizing Compiler for Deep Learning`__, [Slides_]
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.. __: https://arxiv.org/abs/1802.04799
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.. _Slides: https://www.usenix.org/system/files/osdi18-chen.pdf
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* `Learning to Optimize Tensor Programs`__, [Slides]
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.. __: https://arxiv.org/pdf/1805.08166.pdf
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2020
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* `Ansor: Generating High-Performance Tensor Programs for Deep Learning`__, [Slides__] [Tutorial__]
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.. __: https://arxiv.org/abs/2006.06762
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.. __: https://www.usenix.org/sites/default/files/conference/protected-files/osdi20_slides_zheng.pdf
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.. __: https://tvm.apache.org/2021/03/03/intro-auto-scheduler
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2021
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* `Nimble: Efficiently Compiling Dynamic Neural Networks for Model Inference`__, [Slides__]
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.. __: https://arxiv.org/abs/2006.03031
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.. __: https://shenhaichen.com/slides/nimble_mlsys.pdf
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* `Cortex: A Compiler for Recursive Deep Learning Models`__, [Slides__]
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.. __: https://arxiv.org/pdf/2011.01383.pdf
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.. __: https://mlsys.org/media/mlsys-2021/Slides/1507.pdf
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* `UNIT: Unifying Tensorized Instruction Compilation`__, [Slides]
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.. __: https://arxiv.org/abs/2101.08458
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* `Lorien: Efficient Deep Learning Workloads Delivery`__, [Slides]
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.. __: https://assets.amazon.science/c2/46/2481c9064a8bbaebcf389dd5ad75/lorien-efficient-deep-learning-workloads-delivery.pdf
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* `Bring Your Own Codegen to Deep Learning Compiler`__, [Slides] [Tutorial__]
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.. __: https://arxiv.org/abs/2105.03215
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.. __: https://tvm.apache.org/2020/07/15/how-to-bring-your-own-codegen-to-tvm
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2022
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* `DietCode: Automatic optimization for dynamic tensor program`__, [Slides]
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.. __: https://proceedings.mlsys.org/paper/2022/file/fa7cdfad1a5aaf8370ebeda47a1ff1c3-Paper.pdf
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* `Bolt: Bridging the Gap between Auto-tuners and Hardware-native Performance`__, [Slides]
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.. __: https://proceedings.mlsys.org/paper/2022/file/38b3eff8baf56627478ec76a704e9b52-Paper.pdf
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* `The CoRa Tensor Compiler: Compilation for Ragged Tensors with Minimal Padding`__, [Slides]
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.. __: https://arxiv.org/abs/2110.10221
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