Browsing by Author "Lin, Chien"

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A (21150,19050) GC-LDPC Decoder for NAND Flash Applications
(2019-03-01)
Liao, Yen-Chin
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Lin, Chien
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Chang, Hsie-Chia
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Lin, Shu
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電子工程學系及電子研究所
In this paper, a (21150, 19050) globally-coupled low-density parity check (GC-LDPC) code designed for NAND flash memories is presented. The proposed LDPC code comprises three disjoint subcodes which can be decoded independently. This highly structural parity check matrix contributes to efficient decoder implementation and flexible decoding flow control. Moreover, a two-phase local/global decoding procedure optimized for the proposed GC-LDPC code is introduced. Scenarios of collaborative decoding that leverages the special code structures are discussed. In the proposed decoder architecture, the pipelined processing elements with scheduling are employed to reduce the critical path and decoding latency as well. Implemented in UMC 65 nm process, the post-layout simulation shows a maximum decoding throughput of 432 Gb/s with the chip area 3.376 mm(2).
Efficiently mining high utility sequential patterns in static and streaming data
(2017-01-01)
Zihayat, Morteza
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Wu, Cheng-Wei
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An, Aijun
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Tseng, Vincent S.
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Lin, Chien
High utility sequential pattern (HUSP) mining has emerged as a novel topic in data mining. Although some preliminary works have been conducted on this topic, they incur the problem of producing a large search space for high utility sequential patterns. In addition, they mainly focus on mining HUSPs in static databases and do not take streaming data into account, where unbounded data come continuously and often at a high speed. To efficiently deal with both problems, we propose a novel framework for mining high utility sequential patterns over static and streaming databases. In this regard, two efficient data structures named ItemUtilLists (Item Utility Lists) and HUSP-Tree (High Utility Sequential Pattern Tree) are proposed to maintain essential information for mining HUSPs in both offline and online fashions. In addition, a novel utility model called Sequence-Suffix Utility is proposed for effectively pruning the search space in HUSP mining. We propose an algorithm named HUSP-Miner (High Utility Sequential Pattern Miner) to find HUSPs in static databases efficiently. Then, a one-pass algorithm named HUSP-Stream (High Utility Sequential Pattern mining over Data Streams) is proposed to incrementally update ItemUtilLists and HUSP-Tree online and find HUSPs over data streams. To the best of our knowledge, HUSP-Stream is the first method to find HUSPs over data streams. Experimental results on both real and synthetic datasets show that HUSP-Miner outperforms the compared algorithms substantially in terms of execution time, memory usage and number of generated candidates. The experiments also demonstrate impressive performance of HUSP-Stream to update the data structures and discover HUSPs over data streams.