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b) Figure 1. (a) Parity-check matrix H3⨉6, and (b) bipartite graph representation. 1 0 1 1 0 1 0 1 0 0 1 0 0 0 0 1 1 1 H = cn j bn i (a) (b) (c) Figure 2. (a) Base matrix, (b) QC sub-matrix representation with 1 on the lines and 0 otherwise, and (c) full-expanded QC parity-check matrix. S -1 0 S = = = 8 0 -1 12 4 -1 0 -1 e x p a n d i n g f a c t o r p = 16Abstract-This paper shows a triple-mode LDPC decoder design with two design techniques, the matrix reordering algorithm for multi-mode reconfiguration and the Single-Entry-Multiple-Data (SEMD) scheme for throughput enhancement. The matrix reordering algorithm can reduce the computational complexity from O(n!) to O(n 3 ). The SEMD can enhance the throughput by m times with small area overhead. With TSMC 0.13μm CMOS, the proposed design is synthesized in 1.99mm 2 area at 172.4MHz.

Particle filter (PF) is an emerging signal processing methodology, which can effectively deal with nonlinear and non-Gaussian signals by a sample-based approximation of the state probability density function. The particle generation of the PF is a data-independent procedure and can be implemented in parallel. However, the resampling procedure in the PF is a sequential task in natural and difficult to be parallelized. Based on the Amdahl's law, the sequential portion of a task limits the maximum speed-up of the parallelized implementation. Moreover, large particle number is usually required to obtain an accurate estimation, and the complexity of the resampling procedure is highly related to the number of particles. In this article, we propose a multi-prediction (MP) framework with two selection approaches. The proposed MP framework can reduce the required particle number for target estimation accuracy, and the sequential operation of the resampling can be reduced. Besides, the overhead of the MP framework can be easily compensated by parallel implementation. The proposed MP-PF alleviates the global sequential operation by increasing the local parallel computation. In addition, the MP-PF is very suitable for multi-core graphics processing unit (GPU) platform, which is a popular parallel processing architecture. We give prototypical implementations of the MP-PFs on multi-core GPU platform. For the classic bearing-only tracking experiments, the proposed MP-PF can be 25.1 and 15.3 times faster than the sequential importance resampling-PF with 10,000 and 20,000 particles, respectively. Hence, the proposed MP-PF can enhance the efficiency of the parallelization.

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