Zobrazeno 1 - 10 of 142 pro vyhledávání: '"X. Sharon Hu"'
1
Akademický článek
Experimentally validated memristive memory augmented neural network with efficient hashing and similarity search
Autor: Ruibin Mao, Bo Wen, Arman Kazemi, Yahui Zhao, Ann Franchesca Laguna, Rui Lin, Ngai Wong, Michael Niemier, X. Sharon Hu, Xia Sheng, Catherine E. Graves, John Paul Strachan, Can Li
Publikováno v: Nature Communications, Vol 13, Iss 1, Pp 1-13 (2022)
Memory augmented neural network for lifelong on-device learning is bottlenecked by limited bandwidth in conventional hardware. Here, the authors demonstrate its efficient in-memristor realization with a close-software accuracy, supported by hashing a
Externí odkaz: https://doaj.org/article/48aeff7d92ca4dafbe24a9a8135f3359
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2
Akademický článek
Hardware-Software Co-Design of an In-Memory Transformer Network Accelerator
Autor: Ann Franchesca Laguna, Mohammed Mehdi Sharifi, Arman Kazemi, Xunzhao Yin, Michael Niemier, X. Sharon Hu
Publikováno v: Frontiers in Electronics, Vol 3 (2022)
Transformer networks have outperformed recurrent and convolutional neural networks in terms of accuracy in various sequential tasks. However, memory and compute bottlenecks prevent transformer networks from scaling to long sequences due to their high
Externí odkaz: https://doaj.org/article/b47b9b19bb0a4a339b0abbf1811df2de
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3
Akademický článek
VTG-Net: A CNN Based Vessel Topology Graph Network for Retinal Artery/Vein Classification
Autor: Suraj Mishra, Ya Xing Wang, Chuan Chuan Wei, Danny Z. Chen, X. Sharon Hu
Publikováno v: Frontiers in Medicine, Vol 8 (2021)
From diagnosing cardiovascular diseases to analyzing the progression of diabetic retinopathy, accurate retinal artery/vein (A/V) classification is critical. Promising approaches for A/V classification, ranging from conventional graph based methods to
Externí odkaz: https://doaj.org/article/dcf8bea666134b4785802bcac8619162
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4
A Hybrid Optical-Electrical Analog Deep Learning Accelerator Using Incoherent Optical Signals
Autor: Mingdai Yang, Qiuwen Lou, Ramin Rajaei, Mohammad Reza Jokar, Junyi Qiu, Yuming Liu, Aditi Udupa, Frederic T. Chong, John M. Dallesasse, Milton Feng, Lynford L. Goddard, X. Sharon Hu, Yanjing Li
Publikováno v: ACM Journal on Emerging Technologies in Computing Systems. 19:1-24
Optical deep learning (DL) accelerators have attracted significant interests due to their latency and power advantages. In this article, we focus on incoherent optical designs. A significant challenge is that there is no known solution to perform sin
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_________::3b2b14a3d5fabc4a1261148d2266594a
https://doi.org/10.1145/3584183
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7
FeFET Multi-Bit Content-Addressable Memories for In-Memory Nearest Neighbor Search
Autor: Arman Kazemi, Mohammad Mehdi Sharifi, Ann Franchesca Laguna, Franz Muller, Xunzhao Yin, Thomas Kampfe, Michael Niemier, X. Sharon Hu
Publikováno v: IEEE Transactions on Computers
Nearest neighbor (NN) search computations are at the core of many applications such as few-shot learning, classification, and hyperdimensional computing. As such, efficient hardware support for NN search is highly desired. In-memory computing using e
Externí odkaz: https://explore.openaire.eu/search/publication?articleId=doi_dedup___::a2ec39a55cd33ffca94e329af1816688
https://doi.org/10.1109/tc.2021.3136576
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10
Akademický článek
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