Highly parallel and ultra-low-power probabilistic reasoning with programmable gaussian-like memory transistors

Changhyeon Lee, Leila Rahimifard, Junhwan Choi, Jeong-ik Park, Chungryeol Lee, Divake Kumar, Priyesh Shukla, Seung Min Lee, Amit Ranjan Trivedi (), Hocheon Yoo () and Sung Gap Im ()
Additional contact information
Changhyeon Lee: Korea Advanced Institute of Science and Technology (KAIST)
Leila Rahimifard: University of Illinois at Chicago
Junhwan Choi: Dankook University
Jeong-ik Park: Korea Advanced Institute of Science and Technology (KAIST)
Chungryeol Lee: Korea Advanced Institute of Science and Technology (KAIST)
Divake Kumar: University of Illinois at Chicago
Priyesh Shukla: University of Illinois at Chicago
Seung Min Lee: Korea Advanced Institute of Science and Technology (KAIST)
Amit Ranjan Trivedi: University of Illinois at Chicago
Hocheon Yoo: Gachon University
Sung Gap Im: Korea Advanced Institute of Science and Technology (KAIST)

Nature Communications, 2024, vol. 15, issue 1, 1-12

Abstract: Abstract Probabilistic inference in data-driven models is promising for predicting outputs and associated confidence levels, alleviating risks arising from overconfidence. However, implementing complex computations with minimal devices still remains challenging. Here, utilizing a heterojunction of p- and n-type semiconductors coupled with separate floating-gate configuration, a Gaussian-like memory transistor is proposed, where a programmable Gaussian-like current-voltage response is achieved within a single device. A separate floating-gate structure allows for exquisite control of the Gaussian-like current output to a significant extent through simple programming, with an over 10000 s retention performance and mechanical flexibility. This enables physical evaluation of complex distribution functions with the simplified circuit design and higher parallelism. Successful implementation for localization and obstacle avoidance tasks is demonstrated using Gaussian-like curves produced from Gaussian-like memory transistor. With its ultralow-power consumption, simplified design, and programmable Gaussian-like outputs, our 3-terminal Gaussian-like memory transistor holds potential as a hardware platform for probabilistic inference computing.

Date: 2024
References: View complete reference list from CitEc
Citations: View citations in EconPapers (1)

Downloads: (external link)
https://www.nature.com/articles/s41467-024-46681-2 Abstract (text/html)

Related works:
This item may be available elsewhere in EconPapers: Search for items with the same title.

Export reference: BibTeX RIS (EndNote, ProCite, RefMan) HTML/Text

Persistent link: https://EconPapers.repec.org/RePEc:nat:natcom:v:15:y:2024:i:1:d:10.1038_s41467-024-46681-2

Ordering information: This journal article can be ordered from
https://www.nature.com/ncomms/

DOI: 10.1038/s41467-024-46681-2

Access Statistics for this article

Nature Communications is currently edited by Nathalie Le Bot, Enda Bergin and Fiona Gillespie

More articles in Nature Communications from Nature
Bibliographic data for series maintained by Sonal Shukla () and Springer Nature Abstracting and Indexing ().