Neural networks and systems for decoding encoded data

Abstract

Examples described herein utilize multi-layer neural networks to decode encoded data (e.g., data encoded using one or more encoding techniques). The neural networks may have nonlinear mapping and distributed processing capabilities which may be advantageous in many systems employing the neural network decoders. In this manner, neural networks described herein may be used to implement error code correction (ECC) decoders.

What is claimed is: 
     
         1 . An apparatus comprising:
 a first stage of combiners configured to receive encoded input data and further configured to implement a first function to provide first intermediate data; and   a second stage of combiners configured to receive the first intermediate data and further configured to combine the first intermediate data using a set of predetermined weights to provide the encoded data with reduced noise.   
     
     
         2 . The apparatus of  claim 1 , further comprising:
 a third stage of combiners configured to receive the first intermediate data and implement a second function to provide second intermediate data to the second stage of combiners.   
     
     
         3 . The apparatus of  claim 1 , wherein the first function is a nonlinear function. 
     
     
         4 . The apparatus of  claim 1 , wherein the first stage of combiners and second stage of combiners comprises a first plurality of multiplication/accumulation units, the first plurality of multiplication/accumulation units each configured to multiply at least one bit of the encoded input data with at least one of the set of predetermined weights and sum multiple weighted bits of the encoded input data. 
     
     
         5 . The apparatus of  claim 4 , wherein the first stage of combiners further comprises a first plurality of table look-ups, the first plurality of table look-ups each configured to look-up at least one intermediate data value corresponding to an output of a respective one of the first plurality of multiplication/accumulation units based on at least one non-linear function. 
     
     
         6 . The apparatus of  claim 5 , wherein the at least one non-linear function comprises a Gaussian function, a piece-wise linear function, a sigmoid function, a thin-plate-spline function, a multiquadratic function, a cubic approximation, an inverse multi-quadratic function, or combinations thereof. 
     
     
         7 . The apparatus of  claim 1 , wherein the set of predetermined weights is based at least in part on an encoding technique associated with the encoded input data. 
     
     
         8 . The apparatus of  claim 7 , wherein the encoding technique comprises Reed-Solomon coding, Bose-Chaudhuri-Hocquenghem (BCH) coding, low-density parity check (LDPC) coding, Polar coding, or combinations thereof. 
     
     
         9 . The apparatus of  claim 1 , wherein the set of predetermined weights are based on training of a neural network using known noisy encoded data and encoded data pairs. 
     
     
         10 . The apparatus of  claim 1 , further comprising:
 an encoder configured to encode the input data using encoded bits in accordance with an encoding technique and to provide the encoded input data; and   a memory configured to receive the encoded input data from the encoder and configured to store the encoded input data, wherein, in storing the encoded input data, noise is introduced into the encoded input data.   
     
     
         11 . A method comprising:
 transmitting signaling, from a memory of a computing device, indicative of data encoded an encoding technique; and   modifying, at a neural network of the computing device, the data encoded with an encoding technique using a set of weights to provide encoded data with reduced noise.   
     
     
         12 . The method of  claim 11 , further comprising:
 receiving, at the computing device, signaling indicative of a set of encoded data pairs comprising encoded data; and   determining, for the neural network, the set of weights that modifies the encoded data using the signaling indicative of the set of encoded data pairs.   
     
     
         13 . The method of  claim 12 , wherein determining the set of weights comprises selecting weights resulting in a minimized value of an error function between an output of the neural network and known noisy encoded data. 
     
     
         14 . The method of  claim 11 , wherein modifying the data using the neural network using the set of weights to provide encoded data with reduced noise comprises:
 combining the data encoded with the encoding technique among the set of weights to provide the encoded data with reduced noise using a plurality of layers of the neural network, comprising an input layer, a hidden layer, an output layer, or combinations thereof.   
     
     
         15 . The method of  claim 11 , wherein the encoded data with reduced noise is an estimate of the encoded data relative to output of an encoder associated with the encoding technique. 
     
     
         16 . The method of  claim 11 , wherein the encoding technique comprises Reed-Solomon coding, Bose-Chaudhuri-Hocquenghem (BCH) coding, low-density parity check (LDPC) coding, Polar coding, or combinations thereof. 
     
     
         17 . The method of  claim 16 , wherein the neural network is trained multiple times, once for each encoding technique used. 
     
     
         18 . A memory system comprising:
 one or more output buffers configured to transmit noisy output data; and   a neural network configured to receive the noisy output data, and configured to utilize initial weights selected based on encoded data pairs to provide an estimate of encoded data with reduced noise.   
     
     
         19 . The memory system of  claim 18 , wherein the noise is introduced in transmitting the output data from the output buffers. 
     
     
         20 . The memory system of  claim 18 , wherein the neural network is configured to use multiple stages of nodes to provide the estimate of the encoded data, the multiple stages of nodes comprising an input stage, a hidden stage, an output stage, or combinations thereof.