A silicon efficient high speed L = 3 rate 1/2 convolutional decoder using recurrent neural networks

A silicon efficient real-time approach to decode convolutional codes is presented. The algorithm is a special recurrent neural network, which needs no supervision. A standard solution for the convolutional decoding has been the Viterbi algorithm, which is an optimal solution. The complexity of a Viterbi decoder increases exponentially as a function of the constraint length. The complexity of the utilized algorithm increased more likely polynomically, which makes it attractive for applications with a long constraint length. The algorithm requires massive parallel and fast computing, which is hard to achieve effectively using a standard digital logic. Novel floating-gate structures are used to perform highly parallel signal processing within minimal silicon area. Silicon area of a decoder having constraint length of 3 and rate 1/2 is only 950 × 450 µ m²using 0.35 µm CMOS. Measurements show that a BER of 0.06 can be obtained at decoding speed of 1.25 MHz with a input signal having SNR of 0dB.