About Ren Li Ren Li Ph.D. Student, Electrical and Computer Engineering MEMS IC design and fabrication novel devices Ren Li is a Ph.D. candidate working under the supervision of Prof. Hossein Fariborzi in Electrical Engineering program, division of Computer, Electrical and Mathematical Sciences and Engineering (CEMSE), King Abdullah University of Science and Technology (KAUST) in Saudi Arabia. Prior to coming to KAUST, he received his Master's degree in Power Engineering, in August 2015 from the Electrical and Electronic Engineering Department, Nanyang Technological University (NTU) in Singapore with the award of Professional Engineers Board Gold Medal in the Academic year 2014-2015. He also received his Articles Related News January 2019 Approximate Computing With Stochastic Transistors’ Voltage Over-Scaling 1 min read · Tue, Jan 1 2019 News Circuits Transistors Stochastic processes Approximate computing Computational modeling Ren Li, et al., "Approximate Computing With Stochastic Transistors’ Voltage Over-Scaling" IEEE Access 7, 2019, 6373. Ubiquitous computing and the ever-rising need for energy efficiency pose challenges in terms of the processing requirements and the corresponding machine complexity. Nonetheless, the nature of the underlying applications, particularly dealing with real-world data, offers alternative paradigms toward the efficient utilization of the available design resources. In this paper, approximate computing is addressed as an accommodating technique that can benefit from the inherent
Approximate Computing With Stochastic Transistors’ Voltage Over-Scaling 1 min read · Tue, Jan 1 2019 News Circuits Transistors Stochastic processes Approximate computing Computational modeling Ren Li, et al., "Approximate Computing With Stochastic Transistors’ Voltage Over-Scaling" IEEE Access 7, 2019, 6373. Ubiquitous computing and the ever-rising need for energy efficiency pose challenges in terms of the processing requirements and the corresponding machine complexity. Nonetheless, the nature of the underlying applications, particularly dealing with real-world data, offers alternative paradigms toward the efficient utilization of the available design resources. In this paper, approximate computing is addressed as an accommodating technique that can benefit from the inherent
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