Coding Assisted Methods for Crossbar Resistive Memory

Speaker: Zehui Chen
Affiliation: Ph.D. Candidate

Via Zoom:  https://ucla.zoom.us/j/95953534725

Abstract:

Living in the era of big-data, it is crucial to store vast amounts of data and process them quickly. Resistive random-access memory (ReRAM) with the crossbar structure is one promising candidate to be used as the next generation non-volatile memory device and is also one essential enabler for accelerators that can drastically increase data processing speed. In this defense, we tackle problems in crossbar resistive memory and its accelerator application based on channel coding theory and estimation theory.

Biography: 

Zehui Chen is a Ph.D. candidate in the Electrical and Computer Engineering Department at the University of California, Los Angeles (UCLA). He received his M.S. degree in Electrical Engineering from UCLA in 2018. He received his B.S. degree in Electrical Engineering from Purdue University, West Lafayette in 2016. Zehui Chen is currently a graduate student researcher at the Laboratory for Robust Information Systems (LORIS) at UCLA. He worked at Intel as a “Soc Design Engineer Graduate Intern” during summer 2020. He also worked at Samsung Semiconductor as a “Machine Learning and Algorithm Design Intern” and a “Coding Theory Engineering Intern” during summer 2019 and summer 2018 respectively.

For more information, contact Prof. Lara Dolecek (dolecek.ucla@gmail.com)

Date/Time:
Date(s) - May 17, 2021
10:00 am - 12:00 pm

Location:
Via Zoom Only
No location, Los Angeles
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