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Hang-Ting Lue

dblp:87/9742 · DBLP profile ↗
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3ranked-venue papers
1as first author
1since 2021 · last 2021
0000-0003-1078-1333ORCID · corroborated

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

Systems, architecture and hardware · 2 · 1 first-author · 1 since 2021Applied, interdisciplinary, general and emerging computing · 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
Storage systems · 72% Memory systems · 24% Integrated circuit design · 4%

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

TopicWeightPapersLastEvidence papers
Storage systems
flash and SSD
0.212014
On Trading Wear-leveling with Heal-leveling · DAC 2014
Storage systems › storage reliability › durability
flash lifetime
0.212014
On Trading Wear-leveling with Heal-leveling · DAC 2014
Storage systems
storage reliability
0.212014
On Trading Wear-leveling with Heal-leveling · DAC 2014
Storage systems › flash and SSD
flash memory
0.222014
State-of-the-art flash memory devices and post-flash emerging memories · Sci. China Inf. Sci. 2011
On Trading Wear-leveling with Heal-leveling · DAC 2014
Memory systems
emerging memory technologies
0.112011
State-of-the-art flash memory devices and post-flash emerging memories · Sci. China Inf. Sci. 2011
Memory systems
non-volatile memory
0.112011
State-of-the-art flash memory devices and post-flash emerging memories · Sci. China Inf. Sci. 2011
Integrated circuit design
memory circuit design
0.012011
State-of-the-art flash memory devices and post-flash emerging memories · Sci. China Inf. Sci. 2011

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

self-healing technology · 0.2heal-leveling · 0.2
YearPublicationVenuePosition
2021 Design of Computing-in-Memory (CIM) with Vertical Split-Gate Flash Memory for Deep Neural Network (DNN) Inference Accelerator
abstract
Computing-In-Memory (CIM) using Flash memory is a potential solution to support a heavy-weight DNN inference accelerator for edge computing applications. Flash memory provides the best high-density and low-cost non-volatile memory solution to store the weights, while CIM functions of Flash memory can compute AI neural network calculations inside the memory chip. Our analysis indicates that Flash CIM can save data movements by ~85% as compared with the conventional Von-Neumann architecture. In this work, we propose a detail device and design co-optimizations to realize Flash CIM, using a novel vertical split-gate Flash device. Our device supports low-voltage (<; 1V) read at WL's and BL's, tight and tunable cell current (Icell) ranging from 150nA to 1.5uA, extremely large Icell ON/OFF ratio ~ 7 orders, small RTN noise and negligible read disturb to provide a high-performance and highly-reliable CIM solution.
Hang-Ting Lue, Han-Wen Hu, Tzu-Hsuan Hsu, Po-Kai Hsu, Keh-Chung Wang, Chih-Yuan Lu
ISCAS1
2014 On Trading Wear-leveling with Heal-leveling
abstract
Manufacturers are constantly seeking to increase flash memory density in order to fulfill the ever growing demand for storage capacity. However, this trend significantly reduces the reliability and endurance of flash memory chips. The lifetime degradation worsens as the number of erase cycles grows, even with wear leveling technology being adopted to extend flash memory lifetime by evenly distributing erase cycles to every flash block. To address this issue, self-healing technology is proposed to recover a flash block before the flash block is worn out, but such a technology still has its limitation when recovering flash blocks. In contrast to the existing wear leveling designs, we adopt the self-healing technology to propose a heal-leveling design that evenly distributes healing cycles to flash blocks. Ultimately, heal-leveling aims to extend the lifetime of flash memory without introducing a large amount of live-data copying overheads. We conducted a series of experiments to evaluate the capability of the proposed design. The results show that our design can significantly improve the access performance and the effective lifetime of flash memory without the unnecessary overheads caused by wear leveling technology.
Yu-Ming Chang, Yuan-Hao Chang 0001, Jian-Jia Chen, Tei-Wei Kuo, Hsiang-Pang Li, Hang-Ting Lue
DAC6
2011 State-of-the-art flash memory devices and post-flash emerging memories
Chihyuan Lu, Hang-Ting Lue, Yichou Chen
Sci. China Inf. Sci.2