Abstract: Numerous examples are disclosed for an output block coupled to a non-volatile memory array in a neural network and associated methods. In one example, a circuit for converting a current in a neural network into an output voltage comprises a non-volatile memory cell comprises a word line terminal, a bit line terminal, and a source line terminal, wherein the bit line terminal receives the current; and a switch for selectively coupling the word line terminal to the bit line terminal; wherein when the switch is closed, the current flows into the non-volatile memory cell and the output voltage is provided on the bit line terminal.

Abstract: A neural network device with synapses having memory cells each having a floating gate and a first gate over first and second portions of a channel region disposed between source and drain regions, and a second gate over the floating gate or the source region. First lines each electrically connect the first gates in one of the memory cell rows, second lines each electrically connect the second gates in one of the memory cell rows, third lines each electrically connect the source regions in one of the memory cell columns, and fourth lines each electrically connect the drain regions in one of the memory cell columns. The synapses receive a first plurality of inputs as electrical voltages on the first or second lines, and provide a first plurality of outputs as electrical currents on the third or fourth lines.

Abstract: Numerous examples are disclosed for verifying a weight programmed into a selected non-volatile memory cell in a neural memory. In one example, a circuit comprises a digital-to-analog converter to convert a target weight comprising digital bits into a target voltage, a current-to-voltage converter to convert an output current from the selected non-volatile memory cell during a verify operation into an output voltage, and a comparator to compare the output voltage to the target voltage during a verify operation.

Abstract: Numerous embodiments for reading a value stored in a selected memory cell in a vector-by-matrix multiplication (VMM) array in an artificial neural network are disclosed. In one embodiment, an input comprises a set of input bits that result in a series of input pulses applied to a terminal of the selected memory cell, further resulting in a series of output signals that are summed to determine the value stored in the selected memory cell. In another embodiment, an input comprises a set of input bits, where each input bit results in a single pulse or no pulse being applied to a terminal of the selected memory cell, further resulting in a series of output signals which are then weighted according to the binary bit location of the input bit, and where the weighted signals are then summed to determine the value stored in the selected memory cell.

Abstract: A neural network device with synapses having memory cells each having a floating gate and a first gate over first and second portions of a channel region disposed between source and drain regions, and a second gate over the floating gate or the source region. First lines each electrically connect the first gates in one of the memory cell rows, second lines each electrically connect the second gates in one of the memory cell rows, third lines each electrically connect the source regions in one of the memory cell columns, and fourth lines each electrically connect the drain regions in one of the memory cell columns. The synapses receive a first plurality of inputs as electrical voltages on the first or second lines, and provide a first plurality of outputs as electrical currents on the third or fourth lines.

Abstract: Numerous embodiments of a precision programming algorithm and apparatus are disclosed for precisely and quickly depositing the correct amount of charge on the floating gate of a non-volatile memory cell within a vector-by-matrix multiplication (VMM) array in an artificial neural network. Selected cells thereby can be programmed with extreme precision to hold one of N different values.

Abstract: Numerous examples of an input function circuit block and an output neuron circuit block coupled to a vector-by-matrix multiplication (VMM) array in an artificial neural network are disclosed. In one example, an artificial neural network comprises a vector-by-matrix multiplication array comprising a plurality of non-volatile memory cells organized into rows and columns; an input function circuit block to receive digital input signals, convert the digital input signals into analog signals, and apply the analog signals to control gate terminals of non-volatile memory cells in one or more rows of the array during a programming operation; and an output neuron circuit block to receive analog currents from the columns of the array during a read operation and generate an output signal.

Abstract: Numerous embodiments are disclosed for providing temperature compensation in an analog memory array. A method and related system are disclosed for compensating for temperature changes in an array of memory cells by measuring an operating temperature within the array of memory cells and changing a threshold voltage of a selected memory cell in the array of memory cells to compensate for a change in the operating temperature.

Abstract: Numerous examples of a precision tuning algorithm and apparatus are disclosed for precisely and quickly depositing the correct amount of charge on the floating gate of a non-volatile memory cell within a vector-by-matrix multiplication (VMM) array in an artificial neural network. In one example, a method for performing a read or verify operation in a vector-by-matrix multiplication system comprising an input function circuit, a memory array, and an output circuit block is disclosed, the method comprising receiving, by the input function circuit, digital bit input values; converting the digital input values into an input signal; applying the input signal to control gate terminals of selected cells in the memory array; and generating, by the output circuit block, an output value in response to currents received from the memory array.

Abstract: Numerous examples of a precision programming apparatus are disclosed for precisely and quickly depositing the correct amount of charge on the floating gate of a non-volatile memory cell within a vector-by-matrix multiplication (VMM) array in an artificial neural network. In one example, a neuron output circuit for providing a current to program as a weight value in a selected memory cell in a vector-by-matrix multiplication array is disclosed, the neuron output circuit comprising a first adjustable current source to generate a scaled current in response to a neuron current to implement a positive weight, and a second adjustable current source to generate a scaled current in response to a neuron current to implement a negative weight.

Abstract: Numerous embodiments are disclosed for an output circuit for an analog neural memory in a deep learning artificial neural network. In some embodiments, an output block receives current from a W+ bit line and current from an associated W? bit line, and the output block generates an output signal that is a differential signal in certain embodiments and is a single ended signal in other embodiments.

Abstract: Numerous embodiments of a precision tuning algorithm and apparatus are disclosed for precisely and quickly depositing the correct amount of charge on the floating gate of a non-volatile memory cell within a vector-by-matrix multiplication (VMM) array in an artificial neural network. Selected cells thereby can be programmed with extreme precision to hold one of N different values.

Abstract: Configurable input blocks and output blocks and physical layouts are disclosed for analog neural memory systems that utilize non-volatile memory cells. An input block can be configured to support different numbers of arrays arranged in a horizontal direction, and an output block can be configured to support different numbers of arrays arranged in a vertical direction. Adjustable components are disclosed for use in the configurable input blocks and output blocks.

Abstract: Numerous embodiments are disclosed for converting neuron current output by a vector-by-matrix multiplication (VMM) array into neuron current-based time pulses and providing such pulses as an input to another VMM array within an artificial neural network. Numerous embodiments are disclosed for converting the neuron current-based time pulses into analog current or voltage values if an analog input is needed for the VMM array.

Abstract: Numerous embodiments of a precision tuning algorithm and apparatus are disclosed for precisely and quickly depositing the correct amount of charge on the floating gate of a non-volatile memory cell within a vector-by-matrix multiplication (VMM) array in an artificial neural network. Selected cells thereby can be programmed with extreme precision to hold one of N different values.

Abstract: Numerous embodiments of power management techniques are disclosed for various operations involving one or more vector-by-matrix multiplication (VMM) arrays within an artificial neural network.

Abstract: An artificial neural network device that utilizes one or more non-volatile memory arrays as the synapses. The synapses are configured to receive inputs and to generate therefrom outputs. Neurons are configured to receive the outputs. The synapses include a plurality of memory cells, wherein each of the memory cells includes spaced apart source and drain regions formed in a semiconductor substrate with a channel region extending there between, a floating gate disposed over and insulated from a first portion of the channel region and a non-floating gate disposed over and insulated from a second portion of the channel region. Each of the plurality of memory cells is configured to store a weight value corresponding to a number of electrons on the floating gate. The plurality of memory cells are configured to multiply the inputs by the stored weight values to generate the outputs.

Abstract: An artificial neural network device that utilizes one or more non-volatile memory arrays as the synapses. The synapses are configured to receive inputs and to generate therefrom outputs. Neurons are configured to receive the outputs. The synapses include a plurality of memory cells, wherein each of the memory cells includes spaced apart source and drain regions formed in a semiconductor substrate with a channel region extending there between, a floating gate disposed over and insulated from a first portion of the channel region and a non-floating gate disposed over and insulated from a second portion of the channel region. Each of the plurality of memory cells is configured to store a weight value corresponding to a number of electrons on the floating gate. The plurality of memory cells are configured to multiply the inputs by the stored weight values to generate the outputs.

Abstract: A neural network device with synapses having memory cells each having source and drain regions in a semiconductor substrate with a channel region extending there between, a floating gate over an entirety of the channel region, and a first gate over the floating gate. First lines each electrically connect together the first gates in one of the memory cell rows, second lines each electrically connect together the source regions in one of the memory cell rows, and third lines each electrically connect together the drain regions in one of the memory cell columns. The synapses are configured to receive a first plurality of inputs as electrical voltages on the first lines or on the second lines, and to provide a first plurality of outputs as electrical currents on the third lines.

Abstract: A neural network device with synapses having memory cells each having a floating gate and a first gate over first and second portions of a channel region disposed between source and drain regions, and a second gate over the floating gate or the source region. First lines each electrically connect the first gates in one of the memory cell rows, second lines each electrically connect the second gates in one of the memory cell rows, third lines each electrically connect the source regions in one of the memory cell columns, and fourth lines each electrically connect the drain regions in one of the memory cell columns. The synapses receive a first plurality of inputs as electrical voltages on the first or second lines, and provide a first plurality of outputs as electrical currents on the third or fourth lines.