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Min-Gu Kang

dblp:331/3594 · DBLP profile ↗
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1ranked-venue papers
0as first author
1since 2021 · last 2022
0000-0002-1131-8490ORCID · reported

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

Systems, architecture and hardware · 1 · 1 since 2021

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
Memory systems · 94% High-performance computing · 6%

Topics — the 6 heaviest of 6, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Memory systems › processing-in-memory › computing-in-memory
digital compute-in-memory
0.612022
SOT-MRAM Digital PIM Architecture With Extended Parallelism in Matrix Multiplication · IEEE Trans. Computers 2022
Memory systems
non-volatile memory
0.612022
SOT-MRAM Digital PIM Architecture With Extended Parallelism in Matrix Multiplication · IEEE Trans. Computers 2022
Memory systems
processing-in-memory
0.612022
SOT-MRAM Digital PIM Architecture With Extended Parallelism in Matrix Multiplication · IEEE Trans. Computers 2022
Memory systems › non-volatile memory › magnetic random access memory
SOT-MRAM
0.612022
SOT-MRAM Digital PIM Architecture With Extended Parallelism in Matrix Multiplication · IEEE Trans. Computers 2022
Memory systems
in-memory computing
0.212022
SOT-MRAM Digital PIM Architecture With Extended Parallelism in Matrix Multiplication · IEEE Trans. Computers 2022
High-performance computing › numerical linear algebra
matrix multiplication optimization
0.212022
SOT-MRAM Digital PIM Architecture With Extended Parallelism in Matrix Multiplication · IEEE Trans. Computers 2022

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

spintronics-assisted logic-in-memory · 0.6intra-memory pipelining · 0.6VCMA · 0.6
YearPublicationVenuePosition
2022 SOT-MRAM Digital PIM Architecture With Extended Parallelism in Matrix Multiplication
abstract
Emerging device-based digital processing-in-memory (PIM) architectures have been actively studied due to their energy and area efficiency derived from analog to digital converter (ADC)-less PIM hardware. However, digital PIM architectures generally need large extra memories to copy parameters, and they also suffer from low computation per memory-cycle efficiencies. In this paper, we present a novel spin-orbit torque magnetic random access memory (SOT-MRAM) based digital PIM architecture to alleviate the extra memory size burden and computation cycle issues. First, we propose the spintronics-assisted logic-in-memory (SLIM) cells to support efficient digital logic operations inside memories, where the voltage-controlled magnetic anisotropy (VCMA) is exploited to enhance the computation per memory-cycle efficiencies. In addition, crossed input source PIM (CRISP) architecture is proposed to extend the merits of SLIM cells by eliminating the extra memories for parameter copying while significantly improving the degree of parallel processing. An intra-memory pipelining scheme is also considered to further increase the throughput of CRISP. The proposed CRISP architecture has been implemented using 28 nm CMOS process, and it presents 1.10 TOPS/W and 0.95 TOPS/mm2, showing considerable improvements of energy efficiency and throughput per area, compared to the state-of-the-art digital PIM architecture. Finally, to evaluate the impact of computation errors induced from the SOT devices and circuits in CRISP architecture, classification accuracy simulations have been performed while applying computation errors.
Yunho Jang, Min-Gu Kang, Byong-Guk Park, Kyung-Jin Lee, Jongsun Park 0001
IEEE Trans. Computers3