update publications (#1737)

Signed-off-by: chensuyue <suyue.chen@intel.com>

Author: chensuyue

README.md

@@ -195,12 +195,11 @@ quantized_model = fit(model=float_model, conf=static_quant_conf, calib_dataloade
 > Further documentations can be found at [User Guide](https://github.com/intel/neural-compressor/blob/master/docs/source/user_guide.md).
 
 ## Selected Publications/Events
+* Blog by Intel: [Accelerate Meta* Llama 3 with Intel AI Solutions](https://www.intel.com/content/www/us/en/developer/articles/technical/accelerate-meta-llama3-with-intel-ai-solutions.html)
 * Blog by Intel: [Effective Weight-Only Quantization for Large Language Models with Intel® Neural Compressor](https://community.intel.com/t5/Blogs/Tech-Innovation/Artificial-Intelligence-AI/Effective-Weight-Only-Quantization-for-Large-Language-Models/post/1529552) (Oct 2023)
 * EMNLP'2023 (Under Review): [TEQ: Trainable Equivalent Transformation for Quantization of LLMs](https://openreview.net/forum?id=iaI8xEINAf&referrer=%5BAuthor%20Console%5D) (Sep 2023)
 * arXiv: [Efficient Post-training Quantization with FP8 Formats](https://arxiv.org/abs/2309.14592) (Sep 2023)
 * arXiv: [Optimize Weight Rounding via Signed Gradient Descent for the Quantization of LLMs](https://arxiv.org/abs/2309.05516) (Sep 2023)
-* NeurIPS'2022: [Fast Distilbert on CPUs](https://arxiv.org/abs/2211.07715) (Oct 2022)
-* NeurIPS'2022: [QuaLA-MiniLM: a Quantized Length Adaptive MiniLM](https://arxiv.org/abs/2210.17114) (Oct 2022)
 
 > **Note**: 
 > View [Full Publication List](https://github.com/intel/neural-compressor/blob/master/docs/source/publication_list.md).

docs/source/publication_list.md

@@ -1,5 +1,8 @@
-Full Publications/Events (79)
+Full Publications/Events (80)
 ==========
+## 2024 (1)
+* Blog by Intel: [Accelerate Meta* Llama 3 with Intel AI Solutions](https://www.intel.com/content/www/us/en/developer/articles/technical/accelerate-meta-llama3-with-intel-ai-solutions.html) (Apr 2024)
+
 ## 2023 (25)
 * Blog by Intel: [Effective Weight-Only Quantization for Large Language Models with Intel® Neural Compressor](https://community.intel.com/t5/Blogs/Tech-Innovation/Artificial-Intelligence-AI/Effective-Weight-Only-Quantization-for-Large-Language-Models/post/1529552) (Oct 2023)
 * arXiv: [Efficient Post-training Quantization with FP8 Formats](https://arxiv.org/abs/2309.14592) (Sep 2023)