Laura Conde-Canencia

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—Layered decoding is known to provide efficient and high-throughput implementation of LDPC decoders. However, the implementation of the layered architecture is not always straightforward because of the memory access conflicts in the a-posteriori information memory. In this paper, we focus our attention on a particular type of conflict introduced by the(More)
—This paper presents the architecture, performance and implementation results of a serial GF(64)-LDPC decoder based on a reduced-complexity version of the Extended Min-Sum algorithm. The main contributions of this work correspond to the variable node processing, the codeword decision and the elementary check node processing. Post-synthesis area results show(More)
1 Abstract—Non-binary LDPC codes are now recognized as a potential competitor to binary coded solutions, especially when the codeword length is small or moderate. More and more works are reported with good performance/complexity tradeoffs, which make non-binary solutions interesting for practical applications, such as 4G-wireless systems or DVB-like(More)
—Classically, the association of high-order modulation techniques to binary channel coding suffers from significant information loss due to the computation of the channel probabilities at the bit level. In this paper, we investigate the association of Non-Binary Low-Density Parity-Check codes (NB-LDPC) and Cyclic Code-Shift Keying (CCSK) which aims at(More)
Many of the current LDPC implementations of DVB-S2, T2 or WiMAX standard use the so-called layered architecture combined with pipeline. However, the pipeline process may introduce memory access conflicts. The resolution of these conflicts requires careful scheduling combined with dedicated hardware and/or idle cycle insertion. In this paper, based on the(More)
—Associative memories are capable of retrieving previously stored patterns given parts of them. This feature makes them good candidates for pattern detection in images. Clustered Neural Networks is a recently-introduced family of associative memories that allows a fast pattern retrieval when implemented in hardware. In this paper, we propose a new pattern(More)
Layered decoding is known to provide efficient and high-throughput implementation of LDPC decoders. However, two main issues affect performance and area of practical implementations: quantization and memory. Quantization can strongly degrade performance and memory area can constitute up to 70% of the total area of the decoder implementation. This is the(More)
Brain processes information through a complex hierarchical associative memory organization that is distributed across a complex neural network. The GBNN associative memory model has recently been proposed as a new class of recurrent clustered neural network that presents higher efficiency than the classical models. In this article, we propose computational(More)
Associative memories are an alternative to classical indexed memories that are capable of retrieving a message previously stored when an incomplete version of this message is presented. Recently a new model of associative memory based on binary neurons and binary links has been proposed. This model named Clustered Neural Network (CNN) offers large storage(More)