Recent updates (#236)

source/_data/SymbioticLab.bib

@@ -1649,3 +1649,22 @@ @Article{perseus:arxiv23
 In this work, we identify two independent sources of energy bloat in large model training, intrinsic and extrinsic, and propose Perseus, a unified optimization framework that mitigates both. Perseus obtains the "iteration time–energy" Pareto frontier of any large model training job using an efficient iterative graph cut-based algorithm and schedules energy consumption of its forward and backward computations across time to remove intrinsic and extrinsic energy bloat. Evaluation on large models like GPT-3 and Bloom shows that Perseus reduces energy consumption of large model training by up to 30\%, enabling savings otherwise unobtainable before.
   }
 }
+
+@Article{llm-survey:arxiv23,
+  author        = {Zhongwei Wan and Xin Wang and Che Liu and Samiul Alam and Yu Zheng and Zhongnan Qu and Shen Yan and Yi Zhu and Quanlu Zhang and Mosharaf Chowdhury and Mi Zhang},
+  journal       = {CoRR},
+  title         = {Efficient Large Language Models: A Survey},
+  year          = {2023},
+  month         = {Dec},
+  volume        = {abs/2312.03863},
+  archiveprefix = {arXiv},
+  eprint        = {2312.03863},
+  url           = {https://arxiv.org/abs/2312.03863},
+
+  publist_confkey = {arXiv:2312.03863},
+  publist_link = {paper || https://arxiv.org/abs/2312.03863},
+  publist_link = {website || https://github.com/AIoT-MLSys-Lab/Efficient-LLMs-Survey},
+  publist_topic = {Systems + AI},
+  publist_abstract = {
+Large Language Models (LLMs) have demonstrated remarkable capabilities in important tasks such as natural language understanding, language generation, and complex reasoning and have the potential to make a substantial impact on our society. Such capabilities, however, come with the considerable resources they demand, highlighting the strong need to develop effective techniques for addressing their efficiency challenges. In this survey, we provide a systematic and comprehensive review of efficient LLMs research. We organize the literature in a taxonomy consisting of three main categories, covering distinct yet interconnected efficient LLMs topics from model-centric, data-centric, and framework-centric perspective, respectively. We have also created a GitHub repository where we compile the papers featured in this survey, and will actively maintain this repository and incorporate new research as it emerges. We hope our survey can serve as a valuable resource to help researchers and practitioners gain a systematic understanding of the research developments in efficient LLMs and inspire them to contribute to this important and exciting field.  }
+}

source/_posts/Zeus-Accepted-as-a-PyTorch-Ecosystem-project-Congrats-Jae-Won.md

@@ -0,0 +1,7 @@
+---
+title: Zeus accepted as a PyTorch Ecosystem project. Congrats Jae-Won!
+categories:
+  - News
+date: 2023-12-13 21:24:29
+tags:
+---