Circular-buffered architecture for Cellular Neural Networks-based analog Viterbi decoder

The Cellular Neural Network (CNN) based analog Viterbi decoder with a circular-buffered architecture is proposed for decoding partial response maximum likelihood (PRML) signals. The Viterbi decoder is an error correcting method utilizing the dynamic programming which is an efficient algorithm for finding the optimal path with the identical local computation performed at each node. In the previous CNN-based analog Viterbi decoder, a circularly connected cylindrical structure was presented. In this paper, a multiplexer-based cellular 2D structure is presented in which positions of its decoding and output stages are fixed and a multiplexer which distributes input data sequence to appropriate CNN trellis stages is employed. The proposed CNN-based Viterbi decoder is simpler, requires less silicon area, higher speed and has better performance than the previous one. The principle of the new architecture is uncovered and its decoding performance is compared with that of the previous architecture in this paper.