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Gokul Krishnan (Graduate Student Member, IEEE) received the B.Tech. degree in electronics and communication engineering from the Government Model Engineering College, Kochi, India, in 2016. He is currently pursuing the Ph.D. degree in electrical engineering with Arizona State University, Tempe, AZ, USA. His research interests include neuromorphic hardware and microarchitecture design for deep learning applications, chiplet-based hardware architecture for deep learning acceleration, joint algorithm-architecture design for learning on a chip, and performance modeling for CMOS and post-CMOS-based hardware architectures. He is a member of ACM. He was a recipient of the Richard Newton Fellowship at DAC 2020, the Joseph A. Barkson Fellowship for 2020–2021, the GPSA Outstanding Research Award in 2021, and the Engineering Graduate Fellowship in 2022.
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论文共 24 篇作者统计合作学者相似作者
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Gopikrishnan R. Nair, Pragnya S. Nalla,Gokul Krishnan, Anupreetham,Jonghyun Oh,Ahmed Hassan,Injune Yeo, Kishore Kasichainula,Mingoo Seok,Jae-sun Seo,Yu Cao
IEEE Solid-State Circuits Lettersno. 99 (2024): 1-1
Zhenyu Wang, Pragnya Sudershan Nalla,Gokul Krishnan,Rajiv V. Joshi,Nathaniel C. Cady,Deliang Fan,Jae-sun Seo,Yu Cao
2023 International VLSI Symposium on Technology, Systems and Applications (VLSI-TSA/VLSI-DAT)pp.1-4, (2023)
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Springer eBookspp.199-224, (2023)
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Gokul Krishnan, Gopikrishnan Raveendran Nair,Jonghyun Oh,Anupreetham Anupreetham, Pragnya Sudershan Nalla,Ahmed Hassan,Injune Yeo, Kishore Kasichainula,Jae-sun Seo,Mingoo Seok,Yu Cao
2023 IEEE Asian Solid-State Circuits Conference (A-SSCC)pp.1-3, (2023)
Neuromorphic Computing and Engineeringno. 4 (2022)
2022 IEEE 16th International Conference on Solid-State & Integrated Circuit Technology (ICSICT)pp.1-4, (2022)
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