Abstract: Numerous embodiments of analog neural memory arrays are disclosed. Certain embodiments comprise an adaptive bias decoder for providing additional bias to array input lines to compensate for instances where ground floats above 0V. This is useful, for example, to minimize the voltage drop for a read, program, or erase operation while maintaining accuracy in the operation.

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 are disclosed for providing temperature compensation in a an analog memory array. The analog memory array optionally is a vector-by-matrix multiplier in an analog neuromorphic memory system used in a deep learning neural network. One embodiment comprises measuring an operating temperature within a memory array and applying, by a temperature compensation block, a bias voltage to a terminal of a memory cell in the array, wherein the bias voltage is a function of the operating temperature.

Abstract: A number of circuits for use in an output block coupled to a non-volatile memory array in a neural network are disclosed. The embodiments include a circuit for converting an output current from a neuron in a neural network into an output voltage, a circuit for converting a voltage received on an input node into an output current, a circuit for summing current received from a plurality of neurons in a neural network, and a circuit for summing current received from a plurality of neurons in a neural network.

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

Abstract: Numerous embodiments are provided for compensating for drift error in non-volatile memory cells within a VMM array in an analog neuromorphic memory system. For example, in one embodiment, a circuit is provided for compensating for drift error during a read operation, the circuit comprising a data drift monitoring circuit coupled to the array for generating an output indicative of data drift; and a bitline compensation circuit for generating a compensation current in response to the output from the data drift monitoring circuit and injecting the compensation current into one or more bitlines of the array.

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 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 output circuits are disclosed for an analog neural memory system for a deep learning neural network. In one embodiment, an adaptable neuron circuit receives output current from a neuron and converts it into a voltage. In another embodiment, a current sample and hold circuit samples an input current and generates an output current. In another embodiment, a voltage sample and hold circuit samples an input voltage and generates an output voltage.

Abstract: Numerous embodiments of programming, verifying, and reading systems and methods for use with a vector-by-matrix multiplication (VMM) array in an artificial neural network are disclosed. Selected cells can be programmed and verified with extreme precision to hold one of N different values. During a read operation, the system determines which of the N different values is stored in a selected cell.

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 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: 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 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 are disclosed for a configurable hardware system for use in an analog neural memory system for a deep learning neural network. The components within the configurable hardware system that are configurable can include vector-by-matrix multiplication arrays, summer circuits, activation circuits, inputs, reference devices, neurons, and testing circuits. These devices can be configured to provide various layers or vector-by-matrix multiplication arrays of various sizes, such that the same hardware can be used in analog neural memory systems with different requirements.

Abstract: Numerous embodiments of programming systems and methods for use with a vector-by-matrix multiplication (VMM) array in an artificial neural network are disclosed. 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: 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. Systems and methods are utilized for compensating for leakage and offset in the input blocks and output blocks the in analog neural memory systems.

Abstract: Numerous embodiments are disclosed for providing temperature compensation in a an analog memory array. The analog memory array optionally is a vector-by-matrix multiplier in an analog neuromorphic memory system used in a deep learning neural network. One embodiment comprises measuring an operating temperature within a memory array and applying, by a temperature compensation block, a bias voltage to a terminal of a memory cell in the array, wherein the bias voltage is a function of the operating temperature.

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.