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Jongyeon Kim

dblp:144/4734 · DBLP profile ↗
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2ranked-venue papers
1as first author
0since 2021 · last 2015
—ORCID · none

Domains — the database's venue-derived domains; a paper can count in several

Systems, architecture and hardware · 1Software engineering, systems software and programming languages · 1Applied, interdisciplinary, general and emerging computing · 1 · 1 first-author

Expertise — from the expertise taxonomy: the topics of the expert's papers under the CCF categories. A weight counts papers with recency: 1 for a paper about the topic, 0.3 when the topic is its context, halved every five years.

Computer architecture, parallel and distributed computing, and storage systems
1 paper
Emerging computing paradigms · 70% Integrated circuit design · 23% Processor architecture and microarchitecture · 7%

Topics — the 5 heaviest of 5, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Emerging computing paradigms › spintronics › spintronic computing
all-spin logic
0.212015
Spin-Based Computing: Device Concepts, Current Status, and a Case Study on a High-Performance Microprocessor · Proc. IEEE 2015
Emerging computing paradigms › beyond-CMOS computing
beyond-CMOS devices
0.212015
Spin-Based Computing: Device Concepts, Current Status, and a Case Study on a High-Performance Microprocessor · Proc. IEEE 2015
Integrated circuit design
low-power circuit design
0.212015
Spin-Based Computing: Device Concepts, Current Status, and a Case Study on a High-Performance Microprocessor · Proc. IEEE 2015
Emerging computing paradigms
spintronics
0.212015
Spin-Based Computing: Device Concepts, Current Status, and a Case Study on a High-Performance Microprocessor · Proc. IEEE 2015
Processor architecture and microarchitecture
microprocessor design
0.112015
Spin-Based Computing: Device Concepts, Current Status, and a Case Study on a High-Performance Microprocessor · Proc. IEEE 2015

Methods — techniques the papers use, named apart from their topics

system-level power simulation · 0.2device modeling · 0.2
YearPublicationVenuePosition
2015 Spin-Based Computing: Device Concepts, Current Status, and a Case Study on a High-Performance Microprocessor
abstract
As the end draws near for Moore's law, the search for low-power alternatives to complementary metal-oxide-semiconductor (CMOS) technology is intensifying. Among the various post-CMOS candidates, spintronic devices have gained special attention for their potential to overcome the power and performance limitations of CMOS. In particular, all spin logic (ASL) technology, which performs Boolean operations and transfers the output in the spin domain, has been proposed for enabling new capabilities-such as high density, low device count, and nonvolatility-that were previously impossible with CMOS technology. In this paper, first we provide an overview of the history and the current status of the various spintronic devices being pursued by the research community. Then, we describe how spin-based components are integrated into a computing system and the advantages that result. We use a hypothetical spintronic-based Intel Core i7 as a test vehicle to compare the system-level power requirements of ASL- and CMOS-based systems, taking into consideration the unique demands of spin-based interconnects. We conclude with a brief analysis of current limitations and future directions of spintronic research.
Jongyeon Kim, Ayan Paul, Paul A. Crowell, Steven J. Koester, Sachin S. Sapatnekar, Jianping Wang 0006, Chris H. Kim
Proc. IEEE1
2014 Improving STT-MRAM density through multibit error correction
abstract
STT-MRAMs are prone to data corruption due to inadvertent bit flips. Traditional methods enhance robustness at the cost of area/energy by using larger cell sizes to improve the thermal stability of the MTJ cells. This paper employs multibit error correction with DRAM-style refreshing to mitigate errors and provides a methodology for determining the optimal level of correction. A detailed analysis demonstrates that the reduction in nonvolatility requirements afforded by strong error correction translates to significantly lower area for the memory array compared to simpler ECC schemes, even when accounting for the increased overhead of error correction.
Brandon Del Bel, Jongyeon Kim, Chris H. Kim, Sachin S. Sapatnekar
DATE2