Abstract: A neural network includes inputs for receiving input signals, synapses connected to the inputs and having corrective weights, and neurons having outputs connected with the inputs via the synapses. Each neuron generates a neuron sum by summing corrective weights selected from the respective synapse. A controller receives a desired output signal, determines a deviation of the neuron sum from the desired output signal value, and modifies respective corrective weights using the determined deviation. Adding up the modified corrective weights to determine the neuron sum minimizes the deviation and trains the network. A structure-forming module rearranges connections between network elements during the training and a signal allocation module distributes the input signals among the network elements during the training.

Abstract: A matrix processing apparatus having a three-dimensional slice access memory and an input-/output block. The slice access memory includes cells organized into cell slices, each slice storing an entire selected data matrix. The three-dimensional slice access memory is configured to allow read/write access to the entire data matrix at the same time. The input/output block is connected to the three-dimensional slice access memory and is configured to format data into a format acceptable to the three-dimensional slice access memory.

Abstract: A neural network includes inputs for receiving input signals, synapses connected to the inputs and having corrective weights, and neurons having outputs connected with the inputs via the synapses. Each neuron generates a neuron sum by summing corrective weights selected from the respective synapse. A controller receives a desired output signal, determines a deviation of the neuron sum from the desired output signal value, and modifies respective corrective weights using the determined deviation. Adding up the modified corrective weights to determine the neuron sum minimizes the deviation and trains the network. A structure-forming module rearranges connections between network elements during the training and a signal allocation module distributes the input signals among the network elements during the training.

Abstract: A matrix processing apparatus having a three-dimensional slice access memory and an input-/output block. The slice access memory includes cells organized into cell slices, each slice storing an entire selected data matrix. The three-dimensional slice access memory is configured to allow read/write access to the entire data matrix at the same time. The input/output block is connected to the three-dimensional slice access memory and is configured to format data into a format acceptable to the three-dimensional slice access memory.

Abstract: A neural network includes inputs for receiving input signals, and synapses connected to the inputs and having corrective weights organized in an array. Training images are either received by the inputs as an array or codified as such during training of the network. The network also includes neurons, each having an output connected with at least one input via one synapse and generating a neuron sum array by summing corrective weights selected from each synapse connected to the respective neuron. Furthermore, the network includes a controller that receives desired images in an array, determines a deviation of the neuron sum array from the desired output value array, and generates a deviation array. The controller modifies the corrective weight array using the deviation array. Adding up the modified corrective weights to determine the neuron sum array reduces the subject deviation and generates a trained corrective weight array for concurrent network training.

Abstract: A neural network includes inputs for receiving input signals, and synapses connected to the inputs and having corrective weights organized in an array. Training images are either received by the inputs as an array or codified as such during training of the network. The network also includes neurons, each having an output connected with at least one input via one synapse and generating a neuron sum array by summing corrective weights selected from each synapse connected to the respective neuron. Furthermore, the network includes a controller that receives desired images in an array, determines a deviation of the neuron sum array from the desired output value array, and generates a deviation array. The controller modifies the corrective weight array using the deviation array. Adding up the modified corrective weights to determine the neuron sum array reduces the subject deviation and generates a trained corrective weight array for concurrent network training.

Abstract: A neural network includes a plurality of inputs for receiving input signals, and synapses connected to the inputs and having corrective weights established by a memory element that retains a respective weight value. The network additionally includes distributors. Each distributor is connected to one of the inputs for receiving the respective input signal and selects one or more corrective weights in correlation with the input value. The network also includes neurons. Each neuron has an output connected with at least one of the inputs via one synapse and generates a neuron sum by summing corrective weights selected from each synapse connected to the respective neuron. The output of each neuron provides the respective neuron sum to establish operational output signal of the network. A method of operating a neural network includes processing data thereby and using modified corrective weight values established by a separate analogous neural network during training thereof.

Abstract: A neural network includes a plurality of inputs for receiving input signals, and synapses connected to the inputs and having corrective weights established by a memory element that retains a respective weight value. The network additionally includes distributors. Each distributor is connected to one of the inputs for receiving the respective input signal and selects one or more corrective weights in correlation with the input value. The network also includes neurons. Each neuron has an output connected with at least one of the inputs via one synapse and generates a neuron sum by summing corrective weights selected from each synapse connected to the respective neuron. The output of each neuron provides the respective neuron sum to establish operational output signal of the network. A method of operating a neural network includes processing data thereby and using modified corrective weight values established by a separate analogous neural network during training thereof.

Abstract: A neural network includes a plurality of inputs for receiving input signals, and synapses connected to the inputs and having corrective weights. The network additionally includes distributors. Each distributor is connected to one of the inputs for receiving the respective input signal and selects one or more corrective weights in correlation with the input value. The network also includes neurons. Each neuron has an output connected with at least one of the inputs via one synapse and generates a neuron sum by summing corrective weights selected from each synapse connected to the respective neuron. Furthermore, the network includes a weight correction calculator that receives a desired output signal, determines a deviation of the neuron sum from the desired output signal value, and modifies respective corrective weights using the determined deviation. Adding up the modified corrective weights to determine the neuron sum minimizes the subject deviation for training the neural network.

Abstract: A neural network includes a plurality of inputs for receiving input signals, and synapses connected to the inputs and having corrective weights. The network additionally includes distributors. Each distributor is connected to one of the inputs for receiving the respective input signal and selects one or more corrective weights in correlation with the input value. The network also includes neurons. Each neuron has an output connected with at least one of the inputs via one synapse and generates a neuron sum by summing corrective weights selected from each synapse connected to the respective neuron. Furthermore, the network includes a weight correction calculator that receives a desired output signal, determines a deviation of the neuron sum from the desired output signal value, and modifies respective corrective weights using the determined deviation. Adding up the modified corrective weights to determine the neuron sum minimizes the subject deviation for training the neural network.