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De-Yu Shen

dblp:123/2462 · DBLP profile ↗
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3ranked-venue papers
0as 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 · 3Software engineering, systems software and programming languages · 1

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
2 papers
Memory systems · 84% Hardware reliability and fault tolerance · 16%

Topics — the 7 heaviest of 7, each with the papers that count most for it

TopicWeightPapersLastEvidence papers
Memory systems
DRAM
0.322015
SECRET: A Selective Error Correction Framework for Refresh Energy Reduction in DRAMs · ACM Trans. Archit. Code Optim. 2015
NVM duet: unified working memory and persistent store architecture · ASPLOS 2014
Hardware reliability and fault tolerance
error correction
0.212015
SECRET: A Selective Error Correction Framework for Refresh Energy Reduction in DRAMs · ACM Trans. Archit. Code Optim. 2015
Memory systems › DRAM › DRAM refresh
refresh energy reduction
0.212015
SECRET: A Selective Error Correction Framework for Refresh Energy Reduction in DRAMs · ACM Trans. Archit. Code Optim. 2015
Memory systems
non-volatile memory
0.212014
NVM duet: unified working memory and persistent store architecture · ASPLOS 2014
Memory systems › non-volatile memory
persistent data structures
0.212014
NVM duet: unified working memory and persistent store architecture · ASPLOS 2014
Memory systems › non-volatile memory
persistent memory
0.212014
NVM duet: unified working memory and persistent store architecture · ASPLOS 2014
Memory systems › DRAM
energy-efficient DRAM
0.112015
SECRET: A Selective Error Correction Framework for Refresh Energy Reduction in DRAMs · ACM Trans. Archit. Code Optim. 2015

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

selective error correction · 0.2ECC · 0.2
YearPublicationVenuePosition
2015 SECRET: A Selective Error Correction Framework for Refresh Energy Reduction in DRAMs
abstract
DRAMs are used as the main memory in most computing systems today. Studies show that DRAMs contribute to a significant part of overall system power consumption. One of the main challenges in low-power DRAM design is the inevitable refresh process. Due to process variation, memory cells exhibit retention time variations. Current DRAMs use a single refresh period determined by the cell with the largest leakage. Since prolonging refresh intervals introduces retention errors, a set of previous works adopt conventional error-correcting code (ECC) to correct retention errors. However, these approaches introduce significant area and energy overheads. In this article, we propose a novel error correction framework for retention errors in DRAMs, called SECRET (selective error correction for refresh energy reduction). The key observations we make are that retention errors are hard errors rather than soft errors, and only few DRAM cells have large leakage. Therefore, instead of equipping error correction capability for all memory cells as existing ECC schemes, we only allocate error correction information to leaky cells under a refresh interval. Our SECRET framework contains two parts: an offline phase to identify memory cells with retention errors given a target error rate and a low-overhead error correction mechanism. The experimental results show that among all test cases performed, the proposed SECRET framework can reduce refresh power by 87.2% and overall DRAM power up to 18.57% with negligible area and performance overheads.
Chung-Hsiang Lin, De-Yu Shen, Yi-Jung Chen, Chia-Lin Yang, Cheng-Yuan Michael Wang
ACM Trans. Archit. Code Optim.2
2014 NVM duet: unified working memory and persistent store architecture
abstract
Emerging non-volatile memory (NVM) technologies have gained a lot of attention recently. The byte-addressability and high density of NVM enable computer architects to build large-scale main memory systems. NVM has also been shown to be a promising alternative to conventional persistent store. With NVM, programmers can persistently retain in-memory data structures without writing them to disk. Therefore, one can envision that in the future, NVM will play the role of both working memory and persistent store at the same time.
Ren-Shuo Liu, De-Yu Shen, Chia-Lin Yang, Shun-Chih Yu, Cheng-Yuan Michael Wang
ASPLOS2
2012 SECRET: Selective error correction for refresh energy reduction in DRAMs
abstract
DRAMs are used as the main memory in most computing systems today. Studies show that DRAMs contribute to a significant part of overall system power consumption. Therefore, one of the main challenges in low-power DRAM design is the inevitable refresh process. Due to process variation, memory cells exhibit retention time variations. Current DRAMs use a single worst-case refresh period. Prolonging refresh intervals introduces retention errors. Previous works adopt conventional ECC (Error Correcting Code) to correct retention errors. These approaches introduce significant area and energy overheads. In this paper, we propose a novel error correction framework for retention errors in DRAMs, called SECRET (Selective Error Correction for Refresh Energy reducTion). The key observation we make is that retention errors can be treated as hard errors rather than soft errors, and only few DRAM cells have large leakage. Therefore, instead of equipping error correction capability in all memory cells as existing ECC schemes, we only allocate error correction information to leaky cells under a refresh interval. Our SECRET framework contains two parts, an off-line phase to identify memory cells with retention errors given a target error rate, and a low-overhead error correction mechanism. The experimental results show that the proposed SECRET framework can reduce refresh power by 87.2%, and overall DRAM power by 18.57% with negligible area and performance overheads.
Chung-Hsiang Lin, De-Yu Shen, Yi-Jung Chen, Chia-Lin Yang, Cheng-Yuan Michael Wang
ICCD2