Zobrazeno 1 - 10
of 1 183
pro vyhledávání: '"Sun, GUangyu"'
Autor:
Cui, Fan, Yin, Chenyang, Zhou, Kexing, Xiao, Youwei, Sun, Guangyu, Xu, Qiang, Guo, Qipeng, Song, Demin, Lin, Dahua, Zhang, Xingcheng, Yun, Liang
Recent studies have demonstrated the significant potential of Large Language Models (LLMs) in generating Register Transfer Level (RTL) code, with notable advancements showcased by commercial models such as GPT-4 and Claude3-Opus. However, these propr
Externí odkaz:
http://arxiv.org/abs/2407.16237
Theseus: Towards High-Efficiency Wafer-Scale Chip Design Space Exploration for Large Language Models
The emergence of the large language model~(LLM) poses an exponential growth of demand for computation throughput, memory capacity, and communication bandwidth. Such a demand growth has significantly surpassed the improvement of corresponding chip des
Externí odkaz:
http://arxiv.org/abs/2407.02079
Federated learning (FL) enables multiple clients to train models collectively while preserving data privacy. However, FL faces challenges in terms of communication cost and data heterogeneity. One-shot federated learning has emerged as a solution by
Externí odkaz:
http://arxiv.org/abs/2405.01494
Multi-modal transformers mark significant progress in different domains, but siloed high-quality data hinders their further improvement. To remedy this, federated learning (FL) has emerged as a promising privacy-preserving paradigm for training model
Externí odkaz:
http://arxiv.org/abs/2404.12467
Autor:
Zhou, Zhe, Chen, Yiqi, Zhang, Tao, Wang, Yang, Shu, Ran, Xu, Shuotao, Cheng, Peng, Qu, Lei, Xiong, Yongqiang, Sun, Guangyu
The Compute Express Link (CXL) interconnect has provided the ability to integrate diverse memory types into servers via byte-addressable SerDes links. Harnessing the full potential of such heterogeneous memory systems requires efficient memory tierin
Externí odkaz:
http://arxiv.org/abs/2403.18702
Autor:
Chen, Lei, Chen, Yiqi, Chu, Zhufei, Fang, Wenji, Ho, Tsung-Yi, Huang, Ru, Huang, Yu, Khan, Sadaf, Li, Min, Li, Xingquan, Li, Yu, Liang, Yun, Liu, Jinwei, Liu, Yi, Lin, Yibo, Luo, Guojie, Shi, Zhengyuan, Sun, Guangyu, Tsaras, Dimitrios, Wang, Runsheng, Wang, Ziyi, Wei, Xinming, Xie, Zhiyao, Xu, Qiang, Xue, Chenhao, Yan, Junchi, Yang, Jun, Yu, Bei, Yuan, Mingxuan, Young, Evangeline F. Y., Zeng, Xuan, Zhang, Haoyi, Zhang, Zuodong, Zhao, Yuxiang, Zhen, Hui-Ling, Zheng, Ziyang, Zhu, Binwu, Zhu, Keren, Zou, Sunan
Within the Electronic Design Automation (EDA) domain, AI-driven solutions have emerged as formidable tools, yet they typically augment rather than redefine existing methodologies. These solutions often repurpose deep learning models from other domain
Externí odkaz:
http://arxiv.org/abs/2403.07257
Autor:
Yuan, Zhihang, Shang, Yuzhang, Zhou, Yang, Dong, Zhen, Zhou, Zhe, Xue, Chenhao, Wu, Bingzhe, Li, Zhikai, Gu, Qingyi, Lee, Yong Jae, Yan, Yan, Chen, Beidi, Sun, Guangyu, Keutzer, Kurt
The field of efficient Large Language Model (LLM) inference is rapidly evolving, presenting a unique blend of opportunities and challenges. Although the field has expanded and is vibrant, there hasn't been a concise framework that analyzes the variou
Externí odkaz:
http://arxiv.org/abs/2402.16363
Deep neural networks are widely deployed in many fields. Due to the in-situ computation (known as processing in memory) capacity of the Resistive Random Access Memory (ReRAM) crossbar, ReRAM-based accelerator shows potential in accelerating DNN with
Externí odkaz:
http://arxiv.org/abs/2402.06164
In this paper, we introduce a new post-training compression paradigm for Large Language Models (LLMs) to facilitate their wider adoption. We delve into LLM weight low-rank factorization, and find that the challenges of this task stem from the outlier
Externí odkaz:
http://arxiv.org/abs/2312.05821
Personalized Federated Learning (PFL) represents a promising solution for decentralized learning in heterogeneous data environments. Partial model personalization has been proposed to improve the efficiency of PFL by selectively updating local model
Externí odkaz:
http://arxiv.org/abs/2308.09160