Zobrazeno 1 - 10
of 622
pro vyhledávání: '"Kang Mingu"'
Publikováno v:
E3S Web of Conferences, Vol 569, p 26004 (2024)
Geogrids improve pavement performance and extend lifespan by making the unbound aggregate base/subbase course stiffer. Extruded (i.e., punched and drawn) geogrids are widely used in pavement applications. This paper presents an experimental evaluatio
Externí odkaz:
https://doaj.org/article/36b645dd0d2f4df996715780c36a029d
Autor:
Cho, Jungbin, Kim, Junwan, Kim, Jisoo, Kim, Minseo, Kang, Mingu, Hong, Sungeun, Oh, Tae-Hyun, Yu, Youngjae
Human motion, inherently continuous and dynamic, presents significant challenges for generative models. Despite their dominance, discrete quantization methods, such as VQ-VAEs, suffer from inherent limitations, including restricted expressiveness and
Externí odkaz:
http://arxiv.org/abs/2411.19527
Extensive efforts have been made to boost the performance in the domain of language models by introducing various attention-based transformers. However, the inclusion of linear layers with large dimensions contributes to significant computational and
Externí odkaz:
http://arxiv.org/abs/2411.10543
Autor:
Fan, Keming, Moradifirouzabadi, Ashkan, Wu, Xiangjin, Li, Zheyu, Ponzina, Flavio, Persson, Anton, Pop, Eric, Rosing, Tajana, Kang, Mingu
Mass spectrometry (MS) is essential for proteomics and metabolomics but faces impending challenges in efficiently processing the vast volumes of data. This paper introduces SpecPCM, an in-memory computing (IMC) accelerator designed to achieve substan
Externí odkaz:
http://arxiv.org/abs/2411.09760
Autor:
Kang, Mingu, Lee, Dongseok, Cho, Woojin, Park, Jaehyeon, Lee, Kookjin, Gruber, Anthony, Hong, Youngjoon, Park, Noseong
Large language models (LLMs), like ChatGPT, have shown that even trained with noisy prior data, they can generalize effectively to new tasks through in-context learning (ICL) and pre-training techniques. Motivated by this, we explore whether a simila
Externí odkaz:
http://arxiv.org/abs/2410.06442
Autor:
Yang, Haichao, Song, Chang Eun, Xu, Weihong, Khaleghi, Behnam, Mallappa, Uday, Shah, Monil, Fan, Keming, Kang, Mingu, Rosing, Tajana
This paper introduces FSL-HDnn, an energy-efficient accelerator that implements the end-to-end pipeline of feature extraction, classification, and on-chip few-shot learning (FSL) through gradient-free learning techniques in a 40 nm CMOS process. At i
Externí odkaz:
http://arxiv.org/abs/2409.10918
The attention mechanism is a key computing kernel of Transformers, calculating pairwise correlations across the entire input sequence. The computing complexity and frequent memory access in computing self-attention put a huge burden on the system esp
Externí odkaz:
http://arxiv.org/abs/2409.04940
Autor:
Oh, Dongjin, Kang, Junha, Qian, Yuting, Fang, Shiang, Kang, Mingu, Jozwiak, Chris, Bostwick, Aaron, Rotenberg, Eli, Checkelsky, Joseph G., Fu, Liang, Klimczuk, Tomasz, Winiarski, Michal J., Yang, Bohm-Jung, Comin, Riccardo
The pyrochlore lattice, a three-dimensional network of corner-sharing tetrahedra, is a promising material playground for correlated topological phases arising from the interplay between spin-orbit coupling (SOC) and electron-electron interactions. Du
Externí odkaz:
http://arxiv.org/abs/2402.04509
Autor:
Puntel, Denny, Bronsch, Wibke, Tuniz, Manuel, Kang, Mingu, Neves, Paul M., Fang, Shiang, Kaxiras, Efthimios, Checkelsky, Joseph G., Comin, Riccardo, Parmigiani, Fulvio, Cilento, Federico
CoSn is a prototypical kagome compound showing lattice-born flat bands with suppressed bandwidth over large parts of the Brillouin zone. Here, by means of time- and angle-resolved photoelectron spectroscopy, we provide direct evidence of the response
Externí odkaz:
http://arxiv.org/abs/2305.09531