Update paper link and citation

README.md

@@ -8,7 +8,7 @@ ONNXim is a fast cycle-level simulator that can model multi-core NPUs for DNN in
 - Use of ONNX graphs as DNN model specifications, enabling simulation of DNNs implemented in different deep learning frameworks (e.g., PyTorch and TensorFlow).
 - Support language models that do not use ONNX graphs. Additionally, enable auto-regressive generation phases and iteration-level batching.
 
-For more details, please refer to our [paper](https://arxiv.org/abs/2406.08051)!
+For more details, please refer to our [paper](https://ieeexplore.ieee.org/document/10726822)!
 
 ![Speedup](/img/speedup.png)
 **Figure description**: we compare the simulation speed of ONNXim to that of [Accel-sim](https://accel-sim.github.io/) (a GPU simulator with Tensor Core model) as GPUs are widely used for deep learning and such a GPU simulator can be used to study systems for deep learning. We also include [SMAUG](https://github.com/harvard-acc/smaug) in the comparison. On the x-axis, we vary the size of each dimension for an NxNxN GEMM operation.
@@ -186,10 +186,14 @@ This current version only supports GEMM, Conv, Attention, GeLU, LayerNorm operat
 ## Citation
 If you use ONNXim for your research, please cite the following paper.
 ```
-@article{ham2024onnxim,
-  title={ONNXim: A Fast, Cycle-level Multi-core NPU Simulator},
+@ARTICLE{10726822,
   author={Ham, Hyungkyu and Yang, Wonhyuk and Shin, Yunseon and Woo, Okkyun and Heo, Guseul and Lee, Sangyeop and Park, Jongse and Kim, Gwangsun},
-  journal={arXiv preprint arXiv:2406.08051},
-  year={2024}
-}
+  journal={IEEE Computer Architecture Letters}, 
+  title={ONNXim: A Fast, Cycle-Level Multi-Core NPU Simulator}, 
+  year={2024},
+  volume={23},
+  number={2},
+  pages={219-222},
+  keywords={Random access memory;Computational modeling;Vectors;Kernel;Tensors;Runtime;Libraries;Deep learning;Artificial neural networks;Systolic arrays;DNN inference;multi-tenancy;NPU;ONNX;simulator},
+  doi={10.1109/LCA.2024.3484648}}
 ```