Merge branch 'main' into mlbridge-test

Author: RajivChitale

README.md

@@ -3,7 +3,7 @@
 `ML-Compiler-Bridge` is a compiler agnostic library to aid in ML-Enabled Compiler Optimizations. ML-Compiler-Bridge supports both training and inference scenarios. Library exposes Python and C/C++ APIs to interface
 with the Python-based ML models and a C/C++ compiler. This design allows ML model development within a traditional Python framework while making end-to-end integration with an optimizing compiler possible and efficient.
 
-This repo contains the source code and relevant information described in our paper, ["The Next 700 ML-Enabled Compiler Optimizations"](https://arxiv.org/abs/2311.10800).
+This repo contains the source code and relevant information described in our paper, ["The Next 700 ML-Enabled Compiler Optimizations"](https://doi.org/10.1145/3640537.3641580) ([arxiv](https://arxiv.org/abs/2311.10800)).
 Please see [here](https://iith-compilers.github.io/ML-Compiler-Bridge/) for documentation and other details.
 
 > The Next 700 ML-Enabled Compiler Optimizations, S. VenkataKeerthy, Siddharth Jain, Umesh Kalvakuntla, Pranav Sai Gorantla, Rajiv Shailesh Chitale, Eugene Brevdo, Albert Cohen, Mircea Trofin and Ramakrishna Upadrasta. CC 2024.
@@ -77,21 +77,26 @@ target_link_libraries(LLVMMLPass PRIVATE LLVMMLBridge)
 ```
 To use TensorFlow AOT Model Runner, you need to make use of `tf_find_and_compile` method exposed in [`cmake/modules/TensorFlowCompile.cmake`](cmake/modules/TensorFlowCompile.cmake) in the CMakeLists.txt of your pass with appropriate arguments. An example of integrating TF AOT Model with inlining pass is shown [here](https://github.com/IITH-Compilers/ml-llvm-project/blob/tfmodel/llvm/lib/Analysis/CMakeLists.txt).
 
-## Examples
-TBD
-
 ## Artifacts
 Libraries are autogenerated for every relevant check-in with GitHub actions. Such generated artifacts are tagged along with the successful runs of [`Publish`]() action.
 
 ## Citation
 ```
-@misc{venkatakeerthy-2023-MLCompilerBridge,
-      title={The Next 700 ML-Enabled Compiler Optimizations},
-      author={S. VenkataKeerthy and Siddharth Jain and Umesh Kalvakuntla and Pranav Sai Gorantla and Rajiv Shailesh Chitale and Eugene Brevdo and Albert Cohen and Mircea Trofin and Ramakrishna Upadrasta},
-      year={2023},
-      eprint={2311.10800},
-      archivePrefix={arXiv},
-      primaryClass={cs.PL}
+@inproceedings{venkatakeerthy-2024-MLCompilerBridge,
+author = {VenkataKeerthy, S. and Jain, Siddharth and Kalvakuntla, Umesh and Gorantla, Pranav Sai and Chitale, Rajiv Shailesh and Brevdo, Eugene and Cohen, Albert and Trofin, Mircea and Upadrasta, Ramakrishna},
+title = {The Next 700 ML-Enabled Compiler Optimizations},
+year = {2024},
+isbn = {9798400705076},
+publisher = {Association for Computing Machinery},
+address = {New York, NY, USA},
+url = {https://doi.org/10.1145/3640537.3641580},
+doi = {10.1145/3640537.3641580},
+booktitle = {Proceedings of the 33rd ACM SIGPLAN International Conference on Compiler Construction},
+pages = {238–249},
+numpages = {12},
+keywords = {Machine Learning for Compiler Optimizations, ONNX, Pipes, TensorFlow AOT, gRPC},
+location = {<conf-loc>, <city>Edinburgh</city>, <country>United Kingdom</country>, </conf-loc>},
+series = {CC 2024}
 }
 ```