Abstract: The disclosure provides a method for document management in a network. The method includes acquiring, by an electronic device, a source document as an image, extracting, by the electronic device, a plurality of multi-modal information from the source document by parsing the source document, automatically determining, by the electronic device, a category of the source document based on a comparison of the extracted plurality of multi-modal information with a plurality of pre-defined features, extracting, by the electronic device, a plurality of data fields corresponding to the determined category from the source document, determining, by the electronic device, a priority for each of the plurality of data fields and storing, by the electronic device, the plurality of data fields in at least one of a secure information source and an unsecure information source based on the determined priority.

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: 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: The present invention relates to systems and methods for implementing wear leveling in a flash memory device that emulates an EEPROM. The embodiments utilize an index array, which stores an index word for each logical address in the emulated EEPROM. Each bit in each index word is associated with a physical address for a physical word in the emulated EEPROM, and the index word keeps track of which physical word is the current word for a particular logical address. The use of the index word enables a wear leveling algorithm that allows for a programming command to a logical address to result in: (i) skipping the programming operation if the data stored in the current word does not contain a “1” that corresponds to a “0” in the data to be stored, (ii) reprogramming one or more bits of the current word in certain situations, or (iii) shifting to and programming the next physical word in certain situations.

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: Multiple embodiments are disclosed for enhancing security and preventing hacking of a flash memory device. The embodiments prevent malicious actors from hacking a flash memory chip to obtain data that is stored within the chip. The embodiments include the use of fault detection circuits, address scrambling, dummy arrays, password protection, improved manufacturing techniques, and other mechanisms.

Abstract: Numerous embodiments of analog neural memory arrays are disclosed. Two or more physical memory cells are grouped together to form a logical cell that stores one of N possible levels. Within each logical cell, the memory cells can be programmed using different mechanisms. For example, one or more of the memory cells in a logical cell can be programmed using a coarse programming mechanism, one or more of the memory cells can be programmed using a fine mechanism, and one or more of the memory cells can be programmed using a tuning mechanism. This achieves extreme programming accuracy and programming speed.

Abstract: Numerous embodiments of analog neural memory arrays are disclosed. Certain embodiments contain improved mechanisms for pulling source lines down to ground expeditiously. This is useful, for example, to minimize the voltage drop for a read, program, or erase operation.

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 for performing tuning of a page or a word of non-volatile memory cells in an analog neural memory are disclosed. High voltage circuits used to generate high voltages applied to terminals of the non-volatile memory cells during the precision tuning process are also disclosed. Programming sequences for the application of the voltages to the terminals to minimize the occurrence of disturbances during tuning are also disclosed.

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 of analog neural memory arrays are disclosed. In certain embodiments, each memory cell in the array has an approximately constant source impedance when that cell is being operated. In certain embodiments, power consumption is substantially constant from bit line to bit line within the array when cells are being read. In certain embodiments, weight mapping is performed adaptively for optimal performance in power and noise.

Abstract: Numerous embodiments of analog neural memory arrays are disclosed. In one embodiment, an analog neural memory system comprises an array of non-volatile memory cells, wherein the cells are arranged in rows and columns, the columns arranged in physically adjacent pairs of columns, wherein within each adjacent pair one column in the adjacent pair comprises cells storing W+ values and one column in the adjacent pair comprises cells storing W? values, wherein adjacent cells in the adjacent pair store a differential weight, W, according to the formula W=(W+)?(W?). In another embodiment, an analog neural memory system comprises a first array of non-volatile memory cells storing W+ values and a second array storing W? values.

Abstract: Embodiments for ultra-precise tuning of a selected memory cell are disclosed. The selected memory cell optionally is first programmed using coarse programming and fine programming methods. The selected memory cell then undergoes ultra-precise programming through the programming of an adjacent memory cell. As the adjacent memory cell is programmed, capacitive coupling between the floating gate of the adjacent memory cell and the floating gate of the selected memory cell will cause the voltage of the floating gate of the selected memory cell to increase, but in smaller increments than could be achieved by programming the selected memory cell directly. In this manner, the selected memory cell can be programmed with ultra-precise gradations.

Abstract: The present invention relates to systems and methods for implementing wear leveling in a flash memory device that emulates an EEPROM. The embodiments utilize an index array, which stores an index word for each logical address in the emulated EEPROM. Each bit in each index word is associated with a physical address for a physical word in the emulated EEPROM, and the index word keeps track of which physical word is the current word for a particular logical address. The use of the index word enables a wear leveling algorithm that allows for a programming command to a logical address to result in: (i) skipping the programming operation if the data stored in the current word does not contain a “1” that corresponds to a “0” in the data to be stored, (ii) reprogramming one or more bits of the current word in certain situations, or (iii) shifting to and programming the next physical word in certain situations.

Abstract: Numerous embodiments are disclosed for a high voltage generation algorithm and system for generating high voltages necessary for a particular programming operation in analog neural memory used in a deep learning artificial neural network. Compensation measures can be utilized that compensate for changes in voltage or current as the number of cells being programmed changes.

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: A memory device that includes a memory array having pluralities of non-volatile memory cells, a plurality of index memory cells each associated with a different one of the pluralities of the non-volatile memory cells, and a controller. The controller is configured to erase the pluralities of non-volatile memory cells, set each of the index memory cells to a first state, and program first data into the memory array by reading the plurality of index memory cells and determining that a first one of the index memory cells is in the first state, programming the first data into the plurality of the non-volatile memory cells associated with the first one of the index memory cells, and setting the first one of the index memory cells to a second state different from the first state.

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.