Zobrazeno 1 - 10
of 16
pro vyhledávání: '"Jeff Jun Zhang"'
Autor:
Maico Cassel dos Santos, Tianyu Jia, Martin Cochet, Karthik Swaminathan, Joseph Zuckerman, Paolo Mantovani, Davide Giri, Jeff Jun Zhang, Erik Jens Loscalzo, Gabriele Tombesi, Kevin Tien, Nandhini Chandramoorthy, John-David Wellman, David Brooks, Gu-Yeon Wei, Kenneth Shepard, Luca P. Carloni, Pradip Bose
Publikováno v:
Proceedings of the 41st IEEE/ACM International Conference on Computer-Aided Design.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b114290ffc6e7dcf3ef60086d3d23cf0
https://doi.org/10.1145/3508352.3561102
https://doi.org/10.1145/3508352.3561102
Autor:
Tianyu Jia, Paolo Mantovani, Maico Cassel Dos Santos, Davide Giri, Joseph Zuckerman, Erik Jens Loscalzo, Martin Cochet, Karthik Swaminathan, Gabriele Tombesi, Jeff Jun Zhang, Nandhini Chandramoorthy, John-David Wellman, Kevin Tien, Luca Carloni, Kenneth Shepard, David Brooks, Gu-Yeon Wei, Pradip Bose
Publikováno v:
ESSCIRC 2022- IEEE 48th European Solid State Circuits Conference (ESSCIRC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::73b894795562dde71c0576aa79e5d5ee
https://doi.org/10.1109/esscirc55480.2022.9911456
https://doi.org/10.1109/esscirc55480.2022.9911456
Autor:
Cheng Tan, Thierry Tambe, Jeff (Jun) Zhang, Bo Fang, Tong Geng, Gu-Yeon Wei, David Brooks, Antonino Tumeo, Ganesh Gopalakrishnan, Ang Li
Publikováno v:
Proceedings of the 36th ACM International Conference on Supercomputing.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::8b239448eea6635208799e023ce9aae8
https://doi.org/10.1145/3524059.3532359
https://doi.org/10.1145/3524059.3532359
Publikováno v:
2022 Design, Automation & Test in Europe Conference & Exhibition (DATE).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::6425fd6d210dd818e27eb2fc4726058d
https://doi.org/10.23919/date54114.2022.9774697
https://doi.org/10.23919/date54114.2022.9774697
Autor:
Serena Curzel, Nicolas Bohm Agostini, Reece Neff, Ankur Limaye, Jeff Jun Zhang, Vinay Amatya, Marco Minutoli, Vito Giovanni Castellana, Joseph Manzano, David Brooks, Gu-Yeon Wei, Fabrizio Ferrandi, Antonino Tumeo
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::83b05acabfc7bbd9fadaeae8ea6a63a3
https://hdl.handle.net/11311/1229400
https://hdl.handle.net/11311/1229400
Publikováno v:
2021 IEEE 34th International System-on-Chip Conference (SOCC).
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::f4d0771fc57bbb6ddacc05c05d2a0fac
https://doi.org/10.1109/socc52499.2021.9739485
https://doi.org/10.1109/socc52499.2021.9739485
Publikováno v:
ASAP
Brain-inspired hyperdimensional computing (HDC) is an emerging computational paradigm that has achieved success in various domains. HDC mimics brain cognition and lever-ages hyperdimensional vectors with fully distributed holographic representation a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::b95e27758cbc7c3a7a49848621bbd937
https://doi.org/10.1109/asap52443.2021.00039
https://doi.org/10.1109/asap52443.2021.00039
Publikováno v:
IEEE Design & Test. 36:44-53
Editor’s note: Systolic array is embracing its renaissance after being accepted by Google TPU as the core computing architecture of machine learning acceleration. In this article, the authors propose two strategies to enhance fault tolerance of sys
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_________::e98a0650879ee7956f3e425cbbfe3802
https://doi.org/10.1109/mdat.2019.2915656
https://doi.org/10.1109/mdat.2019.2915656
RecPipe: Co-designing Models and Hardware to Jointly Optimize Recommendation Quality and Performance
Autor:
David Brooks, Samuel Hsia, Mark Wilkening, Javin Pombra, Gu-Yeon Wei, Hsien-Hsin S. Lee, Carole-Jean Wu, Jeff Jun Zhang, Udit Gupta
Publikováno v:
MICRO
Deep learning recommendation systems must provide high quality, personalized content under strict tail-latency targets and high system loads. This paper presents RecPipe, a system to jointly optimize recommendation quality and inference performance.
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::f35f998c8c252ba639d2c81f639e44c0
http://arxiv.org/abs/2105.08820
http://arxiv.org/abs/2105.08820
Autor:
Jeff Jun Zhang, Siddharth Garg
Publikováno v:
ICCAD
Deep neural networks (DNN) are increasingly being accelerated on application-specific hardware such as the Google TPU designed especially for deep learning. Timing speculation is a promising approach to further increase the energy efficiency of DNN a
Externí odkaz:
https://explore.openaire.eu/search/publication?articleId=doi_dedup___::cf66d49f9c3faa2f0a0bbfb757f512a0
https://doi.org/10.1145/3240765.3240809
https://doi.org/10.1145/3240765.3240809