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Generalized integrated interleaved (GII) codes nest a set of linear block codewords to generate codewords belonging to stronger codes. They are among the best error-correcting codes for next-generation hyper-speed digital communications and storage. Serial encoders for GII codes based on BCH codes have been previous investigated. They consist of BCH encoders whose inputs and outputs are multiplied by vectors decided by the nesting scheme. However, parallel GII encoders for high-speed systems cannot be designed by directly extending serial encoders due to the unique feature that BCH codes of different error-correcting capabilities are involved. Moreover, GII decoder complexity and latency can be greatly reduced by sharing the encoder to compute short remainders for syndrome computation. Although previous resource-shareable BCH encoders can be utilized to implement resource-shareable GII encoders, they are all serial. This paper first proposes a low-complexity scheme to handle the different error-correcting capabilities of the involved codes and align the input and parity symbols for parallel processing. Then two efficient parallel resource-shareable BCH encoder architectures to be used as GII encoder components are developed. The first design is achieved by deriving parallel register state update formulas for concatenated linear-feedback shift registers (LFSRs). Through reformulating the remainder polynomial divisions, the second design allows the inputs to be added to different LFSR taps, and accordingly reduces the complexity by a significant portion. For an example 160-parallel GII-BCH encoder considered for Flash memory applications, the second proposed design requires 14% smaller area compared to the first one. Besides both of them lead to around 50% latency reduction in the nested syndrome computation with small area overheads compared to the best possible alternative design. [ABSTRACT FROM AUTHOR]

Generalized integrated interleaved (GII) codes nest Reed-Solomon (RS) or BCH sub-codewords to generate codewords belonging to stronger RS or BCH codes. Their hyper-speed decoding and good error-correction capability make them one of the best candidates for next-generation terabit/s digital storage and communications. The key equation solver (KES) in the nested decoding stage causes clock frequency bottleneck and takes a large portion of the GII decoder area. Recent architectures reduce the critical path to two multipliers and rely on the application of the slow-down technique to further reduce it to one. The slow-down technique requires two sub-codewords to be interleaved in the nested KES. However, most of the time, the nested decoding only needs to be carried out on one sub-codeword and half of the clock cycles are wasted. This paper proposes two fast nested KES algorithms, both of which have one multiplier in the critical path without applying slow-down and accordingly reduce the latency of the nested KES to almost a half. The short critical path is achieved by algorithmic reformulations that enable the pre-computation of the scalars in parallel with polynomial updating. Our second design adopts scaled versions of the polynomials to enable product term sharing so that the number of multipliers in each pair of processing elements is reduced from 8 as in the first design to 4. Novel scaling and combined scalar computations are developed to keep the critical path one multiplier. For an example GII code over $GF(2^{8})$ that has 3 nested codewords, our designs achieve 49.9% reduction on the number of clock cycles needed in the nested KES compared to prior designs. Besides, our second design requires 22% less area than the first one under the same timing constraint. [ABSTRACT FROM AUTHOR]

Generalized integrated interleaved (GII) codes nest linear block codewords to generate codewords belonging to stronger linear block codes. They can achieve very high throughput with excellent error-correcting capability. GII codes can be based on either Reed-Solomon (RS) or BCH codes. GII-BCH codes are the most promising candidate for error correction in next-generation terabit/s Flash and storage class memories, and optical communications. On the other hand, GII decoder implementation faces many challenges. In particular, the key equation solver (KES) in the nested decoding process causes not only clock frequency bottleneck but also large area. Although techniques have been developed recently to address these issues for GII-RS codes, they cannot be directly extended for binary GII-BCH codes if every other iteration of the nested KES is skipped to reduce the latency. Two instead of one higher-order syndromes need to be incorporated into each nested KES iteration and this makes the implementation much more challenging. In this paper, algorithmic reformulations and architectural modifications are developed to eliminate the clock frequency bottleneck and reduce the area of GII-BCH nested KES. Additionally, the number of processing elements is substantially reduced by analyzing the minimum number of coefficients to keep for the involved polynomials without undesirable degradation on the error-correcting performance. The critical path of the proposed scaled nested BCH KES architecture with reduced processing elements is only one multiplier and three adders. For an example GII-BCH code over ${GF}(2^{12})$ that has a ${t}_{v}=58$ -error-correcting code as the most powerful code generated by the nesting, our design also further reduces the area by 33%. [ABSTRACT FROM AUTHOR]

Partial label learning (PLL) is a type of weakly supervised learning. This paper proposes a new heuristic algorithm for Partial Label learning through the combination of Feature space And Label space using Error-correcting output codes (PL-FALE for short). In the proposed framework, the feature space of different training sample subsets is exploited to generate diverse positive group and negative group pairs by the divide-and-conquer strategy. Then, the training sample label space is exploited to deal with the partial label overlap in every group pair. A set of experiments are conducted on nine controlled UCI datasets and five real-world datasets, and the experimental results show that PL-FALE can improve the classification performance by utilizing the information embedded in the feature space and label space in most cases. Also, the proposed algorithm achieves more stable performance than other ECOC-based PLL algorithms. The source code of PL-FALE is publicly available for non-commercial and research use at: https://github.com/xiaoziyang1/plfale1. [ABSTRACT FROM AUTHOR]

Recently, symmetric block-wise concatenated-BCH (SBC-BCH) codes are proposed as strong error-correcting codes (ECCs) based on hard-decision channel outputs, which is especially suited for storages using NAND flash memories. Targeting energy-efficient NAND flash memory applications, this paper presents an energy-optimized decoder architecture which includes an iterative decoder for a SBC-BCH code as a main decoder and a low-complexity auxiliary decoder for a block-wise single parity-check (BSPC) code. The auxiliary decoder is opportunistically in action to break the dominant error bound associated with the SBC-BCH code, which allows one to lower the uncorrectable bit-error-rate (UBER) to 10−15 in an energy efficient way. This work presents several design-level optimizations for further enhancing the energy-efficiency of the iterative SBC-BCH decoder. More precisely, the new initialization scheme is proposed for ensuring the energy-efficient seamless decoding scenario. The syndrome tracking is applied to eliminate the previous syndrome calculation and the reordered Chien search further enhances the energy-efficiency as well as the decoding throughput. Targeting a 0.9-rate 4KB SBC-BCH code for commercialized storages using NAND flash memories, a prototype decoder consisting of both the iterative main and auxiliary decoders is designed in a 65-nm CMOS process. By applying the proposed optimizations, the prototype decoder achieves an energy-efficiency of 3.43 pJ/b while providing a decoding throughput of 13.2 Gb/s, which is superior to the previous state-of-the-art decoders for mobile storages. [ABSTRACT FROM AUTHOR]

Generalized integrated interleaved (GII) codes allow localized decoding of short sub-codewords. They are essential to hyper-speed data storage, communications, and continued scaling of distributed storage. Sub-codewords, which are usually Reed–Solomon (RS) or BCH codewords, are nested to generate codewords of higher correction capabilities. If the decoding of individual sub-codewords fails, the higher-order syndromes from the nested codewords are utilized to correct more errors. GII decoder design faces many challenges. The major ones include: 1) high-speed nested decoding utilizing the higher-order syndromes; 2) efficient updating of higher-order syndromes after sub-codewords are corrected; 3) computation of nested syndromes and matrix inversion for converting them to higher-order sub-codeword syndromes; and 4) efficient architectures capable of addressing the variable correction capabilities of the nested codewords. This paper proposes novel algorithmic reformulations and architectural transformations to address each bottleneck. For an example, GII code that has the same rate and length as eight un-nested (255, 223) RS codes, the proposed GII decoder achieves more than seven orders of magnitude improvement in error-correcting performance with less than 30% area overhead compared to the RS decoder. With a critical path of seven XOR gates, the proposed decoder can easily achieve more than 40 GB/s throughput. [ABSTRACT FROM AUTHOR]

Binary financial distress prediction (FDP), which categorizes corporate financial status into the two classes of distress and nondistress, cannot provide enough support for effective financial risk management. This paper focuses on research on multiclass FDP based on the support vector machine (SVM) integrated with the decomposition and fusion methods. Corporate financial status is subdivided into four states: financial soundness, financial pseudosoundness, moderate financial distress and serious financial distress. Three multiclass FDP models are built by integrating the SVM with three decomposition and fusion methods, i.e., one-versus-one (OVO), one-versus-rest (OVR), and error-correcting output coding (ECOC), and they are, respectively called OVO-SVM, OVR-SVM and ECOC-SVM. Empirical research based on data from Chinese listed companies shows that OVO-SVM overall outperforms OVR-SVM and ECOC-SVM and is preferred for multiclass FDP. In addition, all three models trained on the original highly class-imbalanced training dataset cannot obtain satisfying performance, and the data level preprocessing mechanisms that make class distributions balanced in the training dataset can greatly improve their multiclass FDP performance. Compared with multivariate discriminant analysis (MDA) and multinomial logit (MNLogit), OVO-SVM has significantly higher accuracy for financial pseudosoundness and moderate financial distress and lower accuracy for financial soundness and serious financial distress, resulting in no significant difference among their overall multiclass FDP performance. However, OVO-SVM is still more competitive than MDA and MNLogit in that financial pseudosoundness and moderate financial distress are much more difficult to predict by human expertise than the other two financial states. [ABSTRACT FROM AUTHOR]

For high-rate low-density parity-check (LDPC) codes, layered decoding processing can be reordered such that the first-in-first-out (FIFO) buffer that stores variable-to-check (V2C) messages is not needed and, hence, the memory area can be minimized, but at the cost of increased data dependency. This paper presents three techniques that can be used to implement an efficient reordered layered decoder. First, with the assistance of a graph coloring method, the required minimum number of V2C sign memory banks can be theoretically determined, with the corresponding pipeline architecture also designed. After that, the integer linear programming technique is adopted so as to arrange the V2C sign memory banks in a manner that minimizes the number of pipeline stalls, thereby increasing throughput. In order to further simplify the decoder, the first minimum values are not stored if the proposed modified min-sum algorithm is used. The proposed techniques are demonstrated by implementing a rate-0.905 (18396,16644) QC-LDPC decoder using 90-nm CMOS technology. When using the proposed techniques, implementation results show that the throughput-to-area ratio (TAR) increases by 58.9% without sacrificing error-rate performance. [ABSTRACT FROM PUBLISHER]

In this paper, a design approach for architecture- aware nonbinary low-density parity-check convolutional codes (NB-LDPC-CCs) is presented to jointly optimizes the code performance and decoder complexity for achieving high energy-efficiency decoder. The proposed NB-LDPC-CCs not only feature simple structure and low degree, but also compete with other published NB-LDPC-CCs on error-correction capability. With these codes, we present a memory-based layered decoder architecture, where the computation units and the scheduling of the computations are optimized to increase energy efficiency. To demonstrate the feasibility of proposed techniques, a time-varying (50,2,4) NB-LDPC-CC over GF(256) is constructed, and associated decoder is implemented in 90 nm CMOS. The code can reach BER=10^-5 at SNR=0.9\ dB, and support multi code rates with puncturing. Comparing with the state-of-the-art designs, the proposed decoder can save 74% power under the same number of iterations, making it suitable for emerging Internet of Things (IoT) applications. [ABSTRACT FROM AUTHOR]

This paper proposes a novel decoding algorithm to enhance the throughput of turbo decoding in LTE-Advanced systems and its hardware realization. The proposed method completely removes the undesired phase-switching latency by partially overlapping in-ordered and interleaved decoding phases, and as a result, achieves a significant increase of decoding throughput. Moreover, the algorithm does not degrade error-correcting performance for high-rate codes which are essential to achieve the maximum data rate of LTE-Advanced systems. The proposed method called tail-overlapped decoding can be easily applied to the conventional parallel decoding architecture designed for standardized communication systems. In addition, a new structure for the extrinsic information memory is proposed to eliminate memory contentions. A 3GPP LTE-Advanced turbo decoder supporting both decoding methods is implemented in 0.13\mum CMOS technology to show the effectiveness of the proposed algorithm. The decoder exhibits a decoding rate greater than 1Gbps with six iterations, meeting the peak data rate of the LTE-Advanced standard with much less hardware complexity than those of the previous works. [ABSTRACT FROM AUTHOR]

This paper presents an IEEE 802.15.6 compliant soft-decision BCH decoder for energy-constrained wireless body area networks. The proposed soft-decision decoder (SDD) provides a 1 dB coding gain compared to the hard-decision decoder (HDD). The improvement in BER performance can translate into power savings at the transmitter. The energy dissipation and area of the soft-decision BCH decoder is minimized by jointly considering the algorithm, architecture, and circuit parameters. An early termination strategy is proposed to reduce the number of redundant test patterns. Probabilistic sorting is proposed to determine the test patterns, and its hardware complexity is only 54.7% of the conventional sorting method. The HDD kernel is implemented by adopting the Peterson rule, reducing the area by 44.2%. A pass-transistor logic based Chien search circuit consumes 33.3% less energy compared to the standard-cell based implementation. The chip is designed to operate at the minimum energy point of 0.29 V, yielding an energy reduction of 94% compared to a direct-mapped SDD at SNR=5\ dB. Fabricated in 90 nm CMOS, the chip dissipates 5.4 \muW at 500 kHz, achieving a throughput of 6.38 Mbps. [ABSTRACT FROM AUTHOR]

In this work, we present a family of architectures for polar decoders using a reduced-complexity successive-cancellation decoding algorithm that employs unrolling to achieve extremely high throughput values while retaining moderate implementation complexity. The resulting fully-unrolled, deeply-pipelined architecture is capable of achieving a coded throughput in excess of 1 Tbps on a 65 nm ASIC at 500 MHz—three orders of magnitude greater than current state-of-the-art polar decoders. However, unrolled decoders are built for a specific, fixed code. Therefore we also present a new method to enable the use of multiple code lengths and rates in a fully-unrolled polar decoder architecture. This method leads to a length- and rate-flexible decoder while retaining the very high speed typical to unrolled decoders. The resulting decoders can decode a master polar code of a given rate and length, and several shorter codes of different rates and lengths. We present results for two versions of a multi-mode decoder supporting eight and ten different polar codes, respectively. Both are capable of a peak throughput of 25.6 Gbps. For each decoder, the energy efficiency for the longest supported polar code is shown to be of 14.8 pJ/bit at 250 MHz and of 8.8 pJ/bit at 500 MHz. [ABSTRACT FROM AUTHOR]

Blind estimation of forward error correction code parameters at the receiver plays a significant role in non-cooperative communication, adaptive modulation and coding systems, and reconfigurable receiver systems. Turbo convolutional codes, a parallel concatenation of multiple convolutional codes, are used in digital communication and storage systems to achieve low bit error rate. The present paper proposes innovative algorithms for the blind estimation of code parameters and reconstruction of turbo convolutional encoder over noisy scenario. The turbo convolutional code is designed using two component codes along with an interleaver. Recursive systematic convolutional codes are used as component codes. Any imperfection in synchronization of received data for the proposed code parameter estimation algorithm is compensated through a bit position adjustment parameter. The performance of the proposed algorithms in terms of parameter estimation accuracy is investigated for different modulation order, code rate, and constraint length values. It is observed that the performance improves with decrease in modulation order and constraint length values. [ABSTRACT FROM AUTHOR]

The future of quantum repeater networking will require interoperability between various error-correcting codes. A few specific code conversions and even a generalized method are known, however, no detailed analysis of these techniques in the context of quantum networking has been performed. In this paper we analyze a generalized procedure to create Bell pairs encoded heterogeneously between two separate codes used often in error-corrected quantum repeater network designs. We begin with a physical Bell pair and then encode each qubit in a different error-correcting code, using entanglement purification to increase the fidelity. We investigate three separate protocols for preparing the purified encoded Bell pair. We calculate the error probability of those schemes between the Steane [[7,1,3]] code, a distance-3 surface code, and single physical qubits by Monte Carlo simulation under a standard Pauli error model and estimate the resource efficiency of the procedures. A local gate error rate of 10-3 allows us to create high-fidelity logical Bell pairs between any of our chosen codes. We find that a postselected model, where any detected parity flips in code stabilizers result in a restart of the protocol, performs the best. [ABSTRACT FROM AUTHOR]