Update Citation

README.md

@@ -5,6 +5,8 @@ Unified MLIR Computing Frontend for Deep Learning
 Convert Pytorch, Tensorflow, Keras, ONNX models to UFront IR and then lower them into standard MLIR dialect (TOSA IR)
 
 ## Citation
+Guoqing Bao, Heng Shi, Chengyi Cui, Yalin Zhang, and Jianguo Yao. 2024. UFront: Toward A Unified MLIR Frontend for Deep Learning. In 39th IEEE/ACM International Conference on Automated Software Engineering (ASE ’24), October 27-November 1, 2024, Sacramento, CA, USA. ACM, New York, NY, USA, 13 pages. https://doi.org/10.1145/3691620.3695002
+
 ```
 @inproceedings{10.1145/3691620.3695002,
 author = {Bao, Guoqing and Shi, Heng and Cui, Chengyi and Zhang, Yalin and Yao, Jianguo},
@@ -37,9 +39,6 @@ series = {ASE '24}
 
 6. In addition to translating Pytorch, Keras, Tensorflow, and ONNX models into the standard MLIR IR (TOSA), the Rust frontend also provide standard computing workflows including operators, forward, and backward (gradient update for training, future work).
 
-## Citation
-Guoqing Bao, Heng Shi, Chengyi Cui, Yalin Zhang, and Jianguo Yao. 2024. UFront: Toward A Unified MLIR Frontend for Deep Learning. In 39th IEEE/ACM International Conference on Automated Software Engineering (ASE ’24), October 27-November 1, 2024, Sacramento, CA, USA. ACM, New York, NY, USA, 13 pages. https://doi.org/10.1145/3691620.3695002
-
 ## Experiencing UFront without build
 
 Experiencing UFront on Kaggle (for model compilation, performance comparison, ImageNet inference, accuracy validation, etc.)