TY - GEN
T1 - A FeFET based processing-in-memory architecture for solving distributed least-square optimizations
AU - Yoon, Insik
AU - Chang, Muya
AU - Ni, Kai
AU - Jerry, Matthew
AU - Gangopadhyay, Samantak
AU - Smith, Gus
AU - Hamam, Tomer
AU - Narayanan, Vijayakrishan
AU - Romberg, Justin
AU - Lu, Shih Lien
AU - Datta, Suman
AU - Raychowdhury, Arijit
N1 - Publisher Copyright:
© 2018 IEEE.
PY - 2018/8/20
Y1 - 2018/8/20
N2 - Hf02 based ferroelectric FET (FeFET) has recently received great interest for its application in nonvolatile memory (NVM) [1]. Unlike conventional perovskite based ferroelectric materials, Hf02 is CMOS compatible and retains ferroelectricity for thin film with thickness around 10 nm. Therefore, successful integration of ferroelectric Hf02 into advanced CMOS technology makes this technology highly promising for NVM [1]. Moreover, by tuning the portion of switched ferroelectric domain, a FeFET can exhibit multiple intermediate states, which enables its application as an analog conductance in mixed-signal in-memory computing. Currently, such architectures have been applied to neuromorphic computing [2], [3]. In this paper, we present a processing-in-memory (PIM) architecture with FeFETs and demonstrate how this can be used to solve a new class of optimization problems, in particular, distributed least square minimization.
AB - Hf02 based ferroelectric FET (FeFET) has recently received great interest for its application in nonvolatile memory (NVM) [1]. Unlike conventional perovskite based ferroelectric materials, Hf02 is CMOS compatible and retains ferroelectricity for thin film with thickness around 10 nm. Therefore, successful integration of ferroelectric Hf02 into advanced CMOS technology makes this technology highly promising for NVM [1]. Moreover, by tuning the portion of switched ferroelectric domain, a FeFET can exhibit multiple intermediate states, which enables its application as an analog conductance in mixed-signal in-memory computing. Currently, such architectures have been applied to neuromorphic computing [2], [3]. In this paper, we present a processing-in-memory (PIM) architecture with FeFETs and demonstrate how this can be used to solve a new class of optimization problems, in particular, distributed least square minimization.
UR - https://www.scopus.com/pages/publications/85053223410
UR - https://www.scopus.com/pages/publications/85053223410#tab=citedBy
U2 - 10.1109/DRC.2018.8442235
DO - 10.1109/DRC.2018.8442235
M3 - Conference contribution
AN - SCOPUS:85053223410
SN - 9781538630280
T3 - Device Research Conference - Conference Digest, DRC
BT - 2018 76th Device Research Conference, DRC 2018
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 76th Device Research Conference, DRC 2018
Y2 - 24 June 2018 through 27 June 2018
ER -