An analog error correction circuit is disclosed that implements an analog error correction code. The analog circuit includes a crossbar array of memristors or other nonvolatile tunable resistive memory devices. The crossbar array includes a first crossbar array portion programmed with values of a target computation matrix and a second crossbar array portion programmed with values of an encoder matrix for correcting computation errors in the matrix multiplication of an input vector with the computation matrix. The first and second crossbar array portions share the same row lines and are connected to a third crossbar array portion that is programmed with values of a decoder matrix, thereby enabling single-cycle error detection. A computation error is detected based on output of the decoder matrix circuitry and a location of the error is determined via an inverse matrix multiplication operation whereby the decoder matrix output is fed back to the decoder matrix.

A method for decoding data by an electronic device is provided. The method includes receiving, by the electronic device, encoded data, determining, by the electronic device, a sparsity of a plurality of Machine Learning (ML) models of a turbo decoder of the electronic device for decoding the encoded data based on Quality-of-Service (QoS) parameters, and decoding, by the electronic device, the encoded data using the turbo decoder based on the determined sparsity.

A method of telecommunications includes the steps of receiving an encoded block having a plurality of values, dividing the received encoded block into a plurality of received segments, each received segment comprising at least two of the values, decoding each received segment by providing, for each received segment, a plurality of estimated encoded sequences, each estimated encoded sequence comprising at least two data units, merging estimated encoded sequences for consecutive segments to provide a plurality of candidate sequences, and selecting one of the plurality of candidate sequences by performing a closest fit calculation between the received encoded data block and each of the candidate sequences. The method is suitable for use in software-defined radios.

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

An analog error correction circuit is disclosed that implements an analog error correction code. The analog circuit includes a crossbar array of memristors or other non-volatile tunable resistive memory devices. The crossbar array includes a first crossbar array portion programmed with values of a target computation matrix and a second crossbar array portion programmed with values of an encoder matrix for correcting computation errors in the matrix multiplication of an input vector with the computation matrix. The first and second crossbar array portions share the same row lines and are connected to a third crossbar array portion that is programmed with values of a decoder matrix, thereby enabling single-cycle error detection. A computation error is detected based on output of the decoder matrix circuitry and a location of the error is determined via an inverse matrix multiplication operation whereby the decoder matrix output is fed back to the decoder matrix.

An error correction method comprises; when a decoder determines that an input analog code is at a forbidden state, setting a digital binary code as a first predetermined code and inputting the digital binary code to an ECC engine; determining whether the digital binary code has no error or two errors; when the digital binary code has no error, outputting the digital binary code after ECC by the ECC engine; when the digital binary code has two errors, resetting the digital binary code as a second predetermined code and inputting the digital binary code to the ECC engine for ECC; and when the decoder determines that the input analog code is not at the forbidden state, decoding the input analog code into the digital binary code and inputting the digital binary code to the ECC engine for ECC.

Examples described herein utilize multi-layer neural networks, such as multi-layer recurrent neural networks to estimate an error-reduced version of encoded data based on a retrieved version of encoded data (e.g., data encoded using one or more encoding techniques) from a memory. The neural networks and/or recurrent neural networks may have nonlinear mapping and distributed processing capabilities which may be advantageous in many systems employing a neural network or recurrent neural network to estimate an error-reduced version of encoded data for an error correction coding (ECC) decoder, e.g., to facilitate decoding of the error-reduced version of encoded data at the decoder. In this manner, neural networks or recurrent neural networks described herein may be used to improve or facilitate aspects of decoding at ECC decoders, e.g., by reducing errors present in encoded data due to storage or transmission.

A data error correction method, apparatus, device, and readable storage medium are disclosed. The method includes: acquiring target data to be error-corrected; performing error correction on the target data using an error-correcting code to obtain first data; judging whether the performing of the error correction on the target data is successful; responsive to the performing of the error correction on the target data being not successful, correcting the target data using a target neural network to obtain second data, determining the second data as the target data, and continuing to perform the error correction on the target data again; and responsive to the performing of the error correction on the target data being successful, determining the first data as the error-corrected target data.

Systems and methods herein provide for error correction via Low Density Parity Check (LDPC) coding. In one embodiment, a system includes a data buffer operable to receive a block of Low Density Parity Check (LDPC) encoded data. The system also includes a processor operable to reduce a belief propagation algorithm used to encode the LDPC encoded data into a quadratic polynomial, to embed the quadratic polynomial onto a plurality of quantum bits (qubits), and to decode the block of LDPC encoded data via the qubits.

Examples described herein utilize multi-layer neural networks to decode encoded data (e.g., data encoded using one or more encoding techniques). The multi-layer neural networks include an encoder configured to encode input data using encoded bits in accordance with an encoding technique and to provide encoded input data, and a memory configured to receive the encoded input data from the encoder and configured to store the encoded input data. The multi-layer neural networks further include combiners configured to receive the encoded input data from the memory and further configured to combine the encoded input data among a set of predetermined weights. The combiners are further configured to provide encoded data with reduced noise, the noise introduced by the memory.