ProfessorsProfessors of Stanford UniversitySuyog Gupta

Suyog Gupta

Stanford University

H-index: 26

North America-United States

About Suyog Gupta

Suyog Gupta, With an exceptional h-index of 26 and a recent h-index of 21 (since 2020), a distinguished researcher at Stanford University, specializes in the field of Semiconductors, Hardware Acceleration, Machine Learning.

His recent articles reflect a diverse array of research interests and contributions to the field:

Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs

Mobiledets: Searching for object detection architectures for mobile accelerators

Discovering multi-hardware mobile models via architecture search

Accelerator-aware Neural Network Design using AutoML

Acceleration of convolutional neural network training using stochastic perforation

An end-to-end RISC-V solution for ML on the edge using in-pipeline support

Suyog Gupta Information

University	Stanford University
Position	Google IBM T. J. Watson Research Center
Citations(all)	6623
Citations(since 2020)	4736
Cited By	3746
hIndex(all)	26
hIndex(since 2020)	21
i10Index(all)	41
i10Index(since 2020)	31
Email	Access Email
University Profile Page	Stanford University
Google Scholar	View Google Scholar Profile

Suyog Gupta Skills & Research Interests

Semiconductors

Hardware Acceleration

Machine Learning

Top articles of Suyog Gupta

Title	Author(s)	Publication Date
Searching for Efficient Neural Architectures for On-Device ML on Edge TPUs	Berkin Akin Suyog Gupta Yun Long Anton Spiridonov Zhuo Wang ...	2022
Mobiledets: Searching for object detection architectures for mobile accelerators	Yunyang Xiong Hanxiao Liu Suyog Gupta Berkin Akin Gabriel Bender ...	2021
Discovering multi-hardware mobile models via architecture search	Grace Chu Okan Arikan Gabriel Bender Weijun Wang Achille Brighton ...	2021
Accelerator-aware Neural Network Design using AutoML	Berkin Akin Suyog Gupta	2020
Acceleration of convolutional neural network training using stochastic perforation		2020/1/21
An end-to-end RISC-V solution for ML on the edge using in-pipeline support	Zahra Azad Marcia Sahaya Louis Leila Delshadtehrani Anthony Ducimo Suyog Gupta ...	2020