Abstract:
Low-Density Parity-Check (LDPC) codes offer error correction at rates approaching the link channel capacity and reliable and efficient information transfer over bandwidth or return-channel constrained links with data-corrupting noise present. LDPC codes also offer error correction performance approaching channel capacity exponentially fast in terms of the code length, linear processing complexity, and parallelism that scales with the code length. They also offer challenges relating to the decoding complexity of the binary error-correction codes themselves and error floors limiting achievable bit-error rates. A new Relaxed Half-Stochastic (RHS) decoding algorithm is presented that reduces decoding complexity for high decoding throughput applications. The RHS algorithm uses an approach based on stochastic decoding algorithms but differs significantly from the conventional approaches of LDPC decoder implementation. The RHS algorithm also leads to a randomized decoding technique called redecoding that addresses the error floor limitation.
Abstract:
A processor implements a network of functional nodes and communication paths between the nodes. The processor includes a plurality of circuit implementations of the functional nodes of the processor; and a plurality of signal paths implementing the communication paths linking the circuit implementations of the nodes. At least some of the signal paths are configured to pass signal values represented according to temporal patterns of signal levels on the signal paths. The processor also includes a plurality of circuit components for conversion between a signal value represented as a signal level (e.g., voltage or current level) and a signal value represented as a temporal pattern.
Abstract:
Low-Density Parity-Check (LDPC) codes offer error correction at rates approaching the link channel capacity and reliable and efficient information transfer over bandwidth or return-channel constrained links with data-corrupting noise present. LDPC codes also offer error correction performance approaching channel capacity exponentially fast in terms of the code length, linear processing complexity, and parallelism that scales with the code length. They also offer challenges relating to the decoding complexity of the binary error-correction codes themselves and error floors limiting achievable bit-error rates. A new Relaxed Half-Stochastic (RHS) decoding algorithm is presented that reduces decoding complexity for high decoding throughput applications. The RHS algorithm uses an approach based on stochastic decoding algorithms but differs significantly from the conventional approaches of LDPC decoder implementation. The RHS algorithm also leads to a randomized decoding technique called redecoding that addresses the error floor limitation.
Abstract:
Methods for applications such as signal processing, analysis, and coding/decoding replace digital signal processing elements with analog components are implemented by combining soft logic gates and filters, permitting the functionality of complex finite state machines to be implemented.
Abstract:
One embodiment of the invention features a programmable gain stage in analog update circuitry to overcome the accuracy limitation of the circuit gain and the maintenance of small finite number of possible sequence estimates.
Abstract:
An iterative decoder comprising a transconductance amplifier, a sampler, a Min-Sum decoder, and an early determination module is provided. The transconductance amplifier outputs a current proportional to the voltage of the coded bit stream. The sampler converts the amplified current into a plurality of currents and stores the sampled currents in a plurality of buffers. The Min-Sum decoder receives parallel currents, wherein currents represent the message of each variable node. The Min-Sum decoder exchanges the message of variable nodes and check nodes iteratively and outputs a set of decode codewords according to the possibilities. The early terminating module stops the iterative decoding when the decoded codeword converged.
Abstract:
A decoder architecture and method for implementing a decoder are provided. In one implementation, the decoder architecture includes an input buffer configured to receive a plurality of codewords to be processed, and includes an iterative decoder configured to receive a first codeword from the input buffer and process the first codeword. The iterative decoder processes the first codeword only for an amount of time required for the first codeword to become substantially error free. The decoder architecture further includes logic coupled to each of the iterative decoder and the input buffer. The logic is configured to determine when the first codeword processed by the decoder becomes substantially error free. The logic further generates a signal for loading a second codeword from the input buffer into the iterative decoder responsive to the logic determining when the first codeword becomes substantially error free.
Abstract:
The invention relates to a method and an arrangement for decoding a convolutionally encoded signal which comprises code words and the arrangement comprises a neural network which comprises a set of neurons comprising a set of inputs and an output. The received code words are applied to the inputs of the neurons, and the arrangement combines some of the inputs of the neuron in the neuron. In order to enable efficient decoding of the convolutional encoding, some of the output signals of the neural network neurons are fed back to the inputs of the neuron and the neuron multiplies at least some of the inputs of the neuron with one another before combining. The output signal of a predetermined neuron comprises an estimate of a decoded symbol.
Abstract:
A method and circuit for generating an updated metric signal for an analog Viterbi detector is disclosed. A circuit in accordance with the invention comprises a first summing amplifier 11 for subtracting a first reference voltage from a data signal; a second summing amplifier 12 for subtracting a second reference voltage from the data signal; a first comparator 21 for comparing the output of the first summing amplifier with a previous metric signal; a second comparator 22 for comparing the output of the second summing amplifier with the previous metric signal; a first AND gate 41 for combining the output of the first comparator and a first clock signal; a second AND gate 42 for combining the output of the second comparator and the first clock signal; a first sample-and-hold device 51 for sampling the output of the first summing amplifier 11 in response to the output of the first AND gate 41; and a second sample-and-hold device 52 for sampling the output of the second summing amplifier 12 in response to the output of the second AND gate 42. The updated metric signal is output on node 70. A holding capacitor 60 retains a charge associated with the updated metric signal during clock cycles when sample-and-hold circuits 51 and 52 are not triggered.
Abstract:
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.