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See detailEarly-Stopped Approach and Analysis for the Berlekamp-Massey Algorithm
Chou, Hung-Pu UL; Hong-fu, Chou

Report (2022)

BCH codes are being widely used in commercial NAND flash controllers, and the decoding algorithm based on the Berlekamp-Massey (BM) algorithm is a classic solution for solving the key equation used for ... [more ▼]

BCH codes are being widely used in commercial NAND flash controllers, and the decoding algorithm based on the Berlekamp-Massey (BM) algorithm is a classic solution for solving the key equation used for error correction. The latency of BM decoding is the bottleneck of the Bose-Chaudhuri Hocquenghem (BCH) decoder when correcting a high number of bit errors. However, the flash memory has an error distribution that degrades with usage: few errors occur in the new memory and a low number of errors occur within a code block. With usage, the system performance degrades and BM decoding needs t iterations in order to correct a larger number t of errors. In an attempt to improve the system performance for high speed applications, early termination of the BM decoding is necessary to overcome this degradation. In this paper, a practical solution for early termination checking for BM algorithm is provided. The analysis of proposed method is presented by means of considering the weight distribution of BCH code and deriving the probability of malfunction as the event of undetectable error. The proposed method is presented to be effective by the numerical results and the probability of malfunction for the proposed method is lower than 10−26. As a result, the FPGA testing on a USB device validate the reliability of the proposed method for applying to a commercial product. [less ▲]

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See detailLow SLA violation and Low Energy consumption using VM Consolidation in Green Cloud Data Centers
Chou, Hung-Pu UL; Chou, Hong-fu

Report (2020)

Virtual Machines (VM) consolidation is an efficient way towards energy conservation in cloud data centers. The VM consolidation technique is applied to migrate VMs into lesser number of active Physical ... [more ▼]

Virtual Machines (VM) consolidation is an efficient way towards energy conservation in cloud data centers. The VM consolidation technique is applied to migrate VMs into lesser number of active Physical Machines (PMs), so that the PMs which have no VMs can be turned into sleep state. VM consolidation technique can reduce energy consumption of cloud data centers because of the energy consumption by the PM which is in sleep state. Because of VMs sharing the underlying physical resources, aggressive consolidation of VMs can lead to performance degradation. Furthermore, an application may encounter an unexpected resources requirement which may lead to increased response times or even failures. Before providing cloud services, cloud providers should sign Service Level Agreements (SLA) with customers. To provide reliable Quality of Service (QoS) for cloud providers is quite important of considering this research topic. To strike a tradeoff between energy and performance, minimizing energy consumption on the premise of meeting SLA is considered. One of the optimization challenges is to decide which VMs to migrate, when to migrate, where to migrate, and when and which servers to turn on/off. To achieve this goal optimally, it is important to predict the future host state accurately and make plan for migration of VMs based on the prediction. For example, if a host will be overloaded at next time unit, some VMs should be migrated from the host to keep the host from overloading, and if a host will be underloaded at next time unit, all VMs should be migrated from the host, so that the host can be turned off to save power. The design goal of the controller is to achieve the balance between server energy consumption and application performance. Because of the heterogeneity of cloud resources and various applications in the cloud environment, the workload on hosts is dynamically changing over time. It is essential to develop accurate workload prediction models for effective resource management and allocation. The disadvantage of VM consolidation process in cloud data centers is that they only concentrate on primitive system characteristics such as CPU utilization, memory and the number of active hosts. When originating their models and approaches as the decisive factors, these characteristics ignore the discrepancy in performance-to-power efficiency between heterogeneous infrastructures. Therefore, this is the reason that leads to unreasonable consolidation which may cause redundant number of VM migrations and energy waste. Advance artificial intelligence such as reinforcement learning can learn a management strategy without prior knowledge, which enables us to design a model-free resource allocation control system. For example, VM consolidation could be predicted by using artificial intelligence rather than based on the current resources utilization usage [less ▲]

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See detailA Novel Data Packing Technique for QC-LDPC Decoder Architecture applied to NAND flash controller
Chou, Hung-Pu UL; Ma, Longyu; Sham, Chiu-Wing et al

in Global Conference on Consumer Electronics (2019)

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See detailAn Optimization Approach for an RLL-Constrained LDPC Coded Recording System Using Deliberate Flipping
Chou, Hung-Pu UL; Sham, Chiu-Wing; Hong-fu, Chou

in IEEE Communications Letters (2018)

For a recording system that has a run-length-limited (RLL) constraint, this approach imposes the hard error by flipping bits before recording. A high error coding rate limits the correcting capability of ... [more ▼]

For a recording system that has a run-length-limited (RLL) constraint, this approach imposes the hard error by flipping bits before recording. A high error coding rate limits the correcting capability of the RLL bit error. Since iterative decoding does not include the estimation technique, it has the potential capability of solving the hard error bits within several 7 iterations compared to an LDPC coded system. In this letter, we implement density evolution and the differential evolution approach to provide a performance evaluation of unequal error protection LDPC code to investigate the optimal LDPC code distribution for an RLL flipped system. [less ▲]

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See detailUnequal Protection Approach for RLL-constrained LDPC Coded Recording System Using Deliberate Flipping
Chou, Hung-Pu UL; Sham, Chiu-Wing; Hong-fu, Chou

in Proceedings of 2017 International SoC Design Conference (ISOCC) (2017)

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See detailAn RLL-Constrained LDPC Coded Recording System Using Deliberate Flipping and Flipped-Bit Detection
Chou, Hung-Pu UL; Ueng, Yeong-Luh; Lin, Mao-Chao et al

in IEEE Transactions on Communications (2012)

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See detailOn soft iterative decoding for ternary recording systems with RLL constraints
Shih-Kai, Lee; Hsin-Yi, Chen; Mao-Chao, Lin et al

Poster (2010)

In this paper, we investigate the soft iterative decoding technique for ternary recoding systems with run-length-limited (RLL) constraints. We employ a simple binary-to-ternary RLL encoder following the ... [more ▼]

In this paper, we investigate the soft iterative decoding technique for ternary recoding systems with run-length-limited (RLL) constraints. We employ a simple binary-to-ternary RLL encoder following the LDPC (low density parity check) encoder. In the decoder, the iteratively passing of soft information between the LDPC decoder and a detector is used, where the detector is constructed for a combination of the RLL encoder, PLM (pulse length modulation) precoder and the partial response channel. We provide two different decoding algorithms. For one of the decoding algorithm, we are able to obtain bit-error-rate performance which is inferior to the comparable system without considering the RLL constraint for the high sign-to-noise ratio (SNR) regime and is better for the low-to-moderate SNR regime. [less ▲]

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See detailCapacity Approaching Run-Length-Limited Codes for Multilevel Recording Systems
Chou, Hung-Pu UL; Chen, Hsin-Yi; Lin, Mao-Chao et al

in IEEE Transactions on Magnetics (2010)

Detailed reference viewed: 40 (3 UL)