Abstract: In one example, a system comprises a vector-by-matrix multiplication array comprising non-volatile memory cells organized into rows and columns; a plurality of word lines coupled respectively to rows of the vector-by-matrix multiplication array; and a word line driver coupled to the plurality of word lines, the word line driver comprising a plurality of select transistors coupled to a common control line and the plurality of word lines, and a plurality of bias transistors coupled to the plurality of select transistors and capable of providing a bias voltage to a single select transistor in the plurality of select transistors or to all of plurality of select transistors in response to control signals.

Abstract: In one example, a system comprises a programmable logic block comprising programmable logic and a configuration block to store and provide configuration data to the programmable logic, the configuration block comprising a flash memory array to store the configuration data, and the flash memory array comprising an array of split-gate flash memory cells.

Abstract: In one example, an analog neural memory system comprises a vector-by-matrix multiplication array comprising an array of non-volatile memory cells organized into rows and columns, wherein each memory cell comprises a bit line terminal, a control gate terminal, and a word line terminal; a plurality of bit lines, wherein each of the plurality of bit lines is coupled to the bit line terminals of a column of memory cells; a plurality of control gate lines, wherein each of the plurality of control gate lines is coupled to the control gate terminals of a row of memory cells; and a plurality of word lines, wherein each of the plurality of word lines is coupled to the word line terminals of a row of memory cells; wherein the plurality of control gate lines are parallel to the plurality of bit lines and perpendicular to the plurality of word lines.

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: In one example, a neural network device comprises a first plurality of synapses configured to receive a first plurality of inputs and to generate therefrom a first plurality of outputs, wherein the first plurality of synapses comprises a plurality of memory cells, each of the plurality of memory cells configured to store a weight value corresponding to a number of electrons on its floating gate and the plurality of memory cells are configured to generate the first plurality of outputs based upon the first plurality of inputs and the stored weight values.

Abstract: Various examples of decoders and physical layout designs for non-volatile flash memory arrays in an analog neural system are disclosed. In one example, a system comprises a plurality of vector-by-matrix multiplication arrays in an analog neural memory system, each vector-by-matrix multiplication array comprising an array of non-volatile memory cells organized into rows and columns, wherein each memory cell comprises a word line terminal; a plurality of read row decoders, each read row decoder coupled to one of the plurality of vector-by-matrix multiplication arrays for applying a voltage to one or more selected rows during a read operation; and a shared program row decoder coupled to all of the plurality of vector-by-matrix multiplication arrays for applying a voltage to one or more selected rows in one or more of the vector-by-matrix multiplication arrays during a program operation.

Abstract: Numerous embodiments of decoders for use with a vector-by-matrix multiplication (VMM) array in an artificial neural network are disclosed. The decoders include bit line decoders, word line decoders, control gate decoders, source line decoders, and erase gate decoders. In certain embodiments, a high voltage version and a low voltage version of a decoder is used.

Abstract: According to at least one embodiment, one or more potential advertisement slots may be identified based on contextual characteristics relating to content. Such contextual characteristics may include tonality, sentiment and/or data that places potentially worrisome content in context. By identifying potential advertisement slots based on such characteristics, a seller develops a better understanding of content running on its own media platform(s), builds a greater level of trust with its advertiser partners, and generates better yield with respect to its advertisement inventory.

Abstract: In one example, a system comprises a vector-by-matrix multiplication array comprising non-volatile memory cells organized into rows and columns; a plurality of word lines coupled respectively to rows of the vector-by-matrix multiplication array; and a word line driver coupled to the plurality of word lines, the word line driver comprising a plurality of select transistors coupled to a common control line and the plurality of word lines, and a plurality of bias transistors coupled to the plurality of select transistors and capable of providing a bias voltage to a single select transistor in the plurality of select transistors or to all of plurality of select transistors in response to control signals.

Abstract: In one example, a neural network device comprises a first plurality of synapses configured to receive a first plurality of inputs and to generate therefrom a first plurality of outputs, wherein the first plurality of synapses comprises a plurality of memory cells, each of the plurality of memory cells configured to store a weight value corresponding to a number of electrons on its floating gate and the plurality of memory cells are configured to generate the first plurality of outputs based upon the first plurality of inputs and the stored weight values.

Abstract: Numerous embodiments are disclosed for accessing redundant non-volatile memory cells in place of one or more rows or columns containing one or more faulty non-volatile memory cells during a program, erase, read, or neural read operation in an analog neural memory system used in a deep learning artificial neural network.

Abstract: Various examples of decoders and physical layout designs for non-volatile flash memory arrays in an analog neural system are disclosed. In one example, a system comprises a plurality of vector-by-matrix multiplication arrays in an analog neural memory system, each vector-by-matrix multiplication array comprising an array of non-volatile memory cells organized into rows and columns, wherein each memory cell comprises a word line terminal; a plurality of read row decoders, each read row decoder coupled to one of the plurality of vector-by-matrix multiplication arrays for applying a voltage to one or more selected rows during a read operation; and a shared program row decoder coupled to all of the plurality of vector-by-matrix multiplication arrays for applying a voltage to one or more selected rows in one or more of the vector-by-matrix multiplication arrays during a program operation.

Abstract: Various embodiments of word line decoders, control gate decoders, bit line decoders, low voltage row decoders, and high voltage row decoders and various types of physical layout designs for non-volatile flash memory arrays in an analog neural system are disclosed. Shared and segmented embodiments of high voltage row decoders are disclosed.

Abstract: Numerous embodiments for processing the current output of a vector-by-matrix multiplication (VMM) array in an artificial neural network are disclosed. The embodiments comprise a summer circuit and an activation function circuit. The summer circuit and/or the activation function circuit comprise circuit elements that can be adjusted in response to the total possible current received from the VMM to optimize power consumption.

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 of decoders for use with a vector-by-matrix multiplication (VMM) array in an artificial neural network are disclosed. The decoders include bit line decoders, word line decoders, control gate decoders, source line decoders, and erase gate decoders. In certain embodiments, a high voltage version and a low voltage version of a decoder is used.

Abstract: Systems and methods of passing data via resource identifiers for resources are provided herein. A data processing system can receive a first request from a client device responsive to an interaction with a content item presented on a first resource. The content item can include a resource identifier referencing a second resource accessible via an application to be installed. The first request can include the resource identifier and a device identifier. The data processing system can receive a second request from the client device responsive to an execution of the installed application. The second request can include the device identifier and an application identifier. The data processing system can determine that the application is to receive the request identifier. The data processing system can provide the resource identifier to direct the application to access the second resource for presentation via the application.

Abstract: Numerous embodiments of decoders for use with a vector-by-matrix multiplication (VMM) array in an artificial neural network are disclosed. The decoders include bit line decoders, word line decoders, control gate decoders, source line decoders, and erase gate decoders. In certain embodiments, a high voltage version and a low voltage version of a decoder is used.

Abstract: Systems and methods of passing data via resource identifiers for resources are provided herein. A data processing system can receive a first request from a client device responsive to an interaction with a content item presented on a first resource. The content item can include a resource identifier referencing a second resource accessible via an application to be installed. The first request can include the resource identifier and a device identifier. The data processing system can receive a second request from the client device responsive to an execution of the installed application. The second request can include the device identifier and an application identifier. The data processing system can determine that the application is to receive the request identifier. The data processing system can provide the resource identifier to direct the application to access the second resource for presentation via the application.

Abstract: Systems and methods of passing data via resource identifiers for resources are provided herein. A data processing system can receive a first request from a client device responsive to an interaction with a content item presented on a first resource. The content item can include a resource identifier referencing a second resource accessible via an application to be installed. The first request can include the resource identifier and a device identifier. The data processing system can receive a second request from the client device responsive to an execution of the installed application. The second request can include the device identifier and an application identifier. The data processing system can determine that the application is to receive the request identifier. The data processing system can provide the resource identifier to direct the application to access the second resource for presentation via the application.