Abstract: Aspects for generating a dot product for two vectors in neural network are described herein. The aspects may include a controller unit configured to receive a vector load instruction that includes a first address of a first vector and a length of the first vector. The aspects may further include a direct memory access unit configured to retrieve the first vector from a storage device based on the first address of the first vector. Further still, the aspects may include a caching unit configured to store the first vector.

Abstract: Aspects for executing forward propagation of artificial neural network are described here. As an example, the aspects may include a plurality of computation modules connected via an interconnection unit; and a controller unit configured to decode an instruction into one or more groups of micro-instructions, wherein the plurality of computation modules are configured to perform respective groups of the micro-instructions.

Abstract: In an example embodiment, two machine learned models are trained. One is trained to output a probability that a searcher having a member profile in a social networking service will select a potential search result. The other is trained to output a probability that a member corresponding to a potential search result will respond to a communication from a searcher. Features may be extracted from a query, information about the searcher, and information about the member corresponding to the potential search result and fed to the machine learned models. The outputs of the machine learned models can be combined and used to rank search results for returning to the searcher.

Abstract: Aspects of data compression/decompression for neural networks are described herein. The aspects may include a model data converter configured to convert neural network content values into pseudo video data. The neural network content values may refer to weight values and bias values of the neural network. The pseudo video data may include one or more pseudo frames. The aspects may further include a compression module configured to encode the pseudo video data into one or more neural network data packages.

Abstract: The present invention discloses a lithium-ion battery electrolyte and a lithium-ion battery. The electrolyte comprises an organic non-aqueous solution, a lithium salt, and an additive. The additive comprises: (A) fluoroethylene carbonate; (B) at least one compound from the following: a saturated dinitrile or an unsaturated nitrile as represented by structural formula (1), wherein R1 is an unsaturated hydrocarbon group with 3-6 carbon atoms and R2 is an alkene group with 2-5 carbon atoms; and (C) at least one unsaturated phosphate ester as represented by structural formula (2), wherein R3, R4, and R5 are each a hydrocarbon with 1-4 carbon atoms, and at least one of R3, R4, and R5 contain an unsaturated hydrocarbon with a triple bond.

Abstract: Techniques for processing search queries are described. Consistent with some embodiments, a computer system generates a profile vector representation for each of several user profiles based on the user profile data of the user profiles, and then stores the vector representations. A subsequent query is processed to generate a query vector representation for the query. A neural network is used to generate a similarity score for each pairing of the query vector representation and a profile vector representation. Finally, some number of user profiles having the highest similarity scores are provided as search results.

Abstract: Disclosed is a lithium ion battery using a non-aqueous electrolyte. The lithium ion battery comprises a positive electrode, a negative electrode, a separator arranged between the positive electrode and the negative electrode, and a non-aqueous electrolyte of the lithium ion battery. An active substance of the positive electrode comprises LiFePO4; the non-aqueous electrolyte of the lithium ion battery comprises a non-aqueous organic solvent, a lithium salt and an additive; and the additive at least comprises (A) vinylene carbonate, and at the same time, also comprises at least one of (B) a compound shown in a structural formula 1 and (C) a fluorobenzene, wherein n is a natural number of 1-3, and each of R1, R2, R3 and R4 is independently selected from one of a hydrogen atom, a fluorine atom, and an alkyl group with 1-6 carbon atoms. The lithium ion battery has a long cycle life, and at the same time, the battery has excellent high and low temperature performance.

Abstract: A method of rendering an animated object includes: (1) determining momentums of a plurality of particles of the object as sums of polynomials; (2) transferring the momentums of the particles of the object to a grid including a plurality of grid nodes; (3) updating momentums of the grid nodes based on the transferred momentums of the particles; (4) transferring the updated momentums of the grid nodes to the particles of the object; (5) updating positions of the particles based on the updated momentums of the grid nodes; and (6) outputting a visualization of the object based on the updated positions of the particles of the object.

Abstract: The present disclosure relates to searching for and committing low-frequency data to a database. An example method generally includes receiving, from a requesting application, a query for data from the data repository. A database system retrieves a set of indices associated with the data specified in the query from an index table in the data repository. Upon determining that the set of indices comprises a non-null set, the database system retrieves records associated with each index in the set of indices from a data table associated with the index table and returns the retrieved records to the requesting application.

Abstract: A compression coding apparatus for artificial neural network, including memory interface unit, instruction cache, controller unit and computing unit, wherein the computing unit is configured to perform corresponding operation to data from the memory interface unit according to instructions of controller unit; the computing unit mainly performs three steps operation: step one is to multiply input neuron by weight data; step two is to perform adder tree computing and add the weighted output neuron obtained in step one level-by-level via adder tree, or add bias to output neuron to get biased output neuron; step three is to perform activation function operation to get final output neuron. The present disclosure also provides a method for compression coding of multi-layer neural network.

Abstract: In an example, a plurality of user profiles in a social networking service are accessed. A heterogeneous graph structure having a plurality of nodes connected by edges is generated, each node corresponding to a different entity in the social networking service, each edge representing a co-occurrence of entities represented by nodes on each side of the edge in at least one of the user profiles. Weights are calculated for each edge of the heterogeneous graph structure, the weights being based on co-occurrence counts reflecting a number of user profiles in the plurality of user profiles in which corresponding nodes co-occurred. The heterogeneous graph structure is embedded into a d-dimensional space. A machine-learned model is then used to calculate a similarity score between a first node and second node by computing distance between the first node and the second node in the d¬-dimensional space.

Abstract: Embodiments of the present disclosure relate to multi-band FDD transceivers. An example transceiver includes a LO, configured to generate a LO signal to be shared between a receiver and a transmitter of the transceiver. Both the receiver and the transmitter use quadrature signal processing and are configured to multi-band operation. Sharing a single LO to perform frequency conversion of different frequency bands of received and transmitted signals advantageously allows reducing the number of LOs used in a multi-band FDD transceiver.

Abstract: An apparatus for executing backpropagation of an artificial neural network comprises an instruction caching unit, a controller unit, a direct memory access unit, an interconnection unit, a master computation module, and multiple slave computation modules. For each layer in a multilayer neural network, weighted summation may be performed on input gradient vectors to calculate an output gradient vector of this layer. The output gradient vector may be multiplied by a derivative value of a next-layer activation function on which forward operation is performed, so that a next-layer input gradient vector can be obtained. The input gradient vector may be multiplied by an input neuron counterpoint in forward operation to obtain the gradient of a weight value of this layer, and the weight value of this layer can be updated according to the gradient of the obtained weight value of this layer.

Abstract: An apparatus for executing backpropagation of an artificial neural network comprises an instruction caching unit, a controller unit, a direct memory access unit, an interconnection unit, a master computation module, and multiple slave computation modules. For each layer in a multilayer neural network, weighted summation may be performed on input gradient vectors to calculate an output gradient vector of this layer. The output gradient vector may be multiplied by a derivative value of a next-layer activation function on which forward operation is performed, so that a next-layer input gradient vector can be obtained. The input gradient vector may be multiplied by an input neuron counterpoint in forward operation to obtain the gradient of a weight value of this layer, and the weight value of this layer can be updated according to the gradient of the obtained weight value of this layer.

Abstract: A method may include: partitioning data on an on-chip and/or an off-chip storage medium into different data blocks according to a pre-determined data partitioning principle, wherein data with a reuse distance less than a pre-determined distance threshold value is partitioned into the same data block; and a data indexing step for successively loading different data blocks to at least one on-chip processing unit according a pre-determined ordinal relation of a replacement policy, wherein the repeated data in a loaded data block being subjected to on-chip repetitive addressing. Data with a reuse distance less than a pre-determined distance threshold value is partitioned into the same data block, and the data partitioned into the same data block can be loaded on a chip once for storage, and is then used as many times as possible, so that the access is more efficient.

Abstract: A method for soft start of a motor in a heating, ventilation, and cooling (HVAC) system. The method includes operably connecting a first switching device with the motor; the first switching device operably connected to a power source and configured to direct power from a DC power source to the motor, and operably connecting a second switching device with the motor, the second switching device operably connected to a power source and configured to direct power from the DC power source to the motor. The method also includes receiving, by a controller, a request to engage the motor associated with an operation of the HVAC system, commanding the first switching device with a first pulse width modulation signal for a first selected duration, while ensuring the second switching device is disengaged, and after the first selected duration, enabling the second switching device and disengaging the first switching device.

Abstract: Aspects for backpropagation of a convolutional neural network are described herein. The aspects may include a direct memory access unit configured to receive input data from a storage device and a master computation module configured to select one or more portions of the input data based on a predetermined convolution window. Further, the aspects may include one or more slave computation modules respectively configured to convolute one of the one or more portions of the input data with one of one or more previously calculated first data gradients to generate a kernel gradient, wherein the master computation module is further configured to update a prestored convolution kernel based on the kernel gradient.

Abstract: Aspects of a neural network convolution device are described herein. The aspects may include a matrix transformer and a matrix multiplication module. The matrix transformer may be configured to receive an input data matrix and a weight matrix, transform the input data matrix into a transformed input data matrix based on a first transformation matrix, and transform the weight matrix into a transformed weight matrix based on a second transformation matrix. The matrix multiplication module may be configured to multiply one or more input data elements in the transformed input data matrix with one or more weight elements in the transformed weight matrix to generate an intermediate output matrix. The matrix transformer may be further configured to transform the intermediate output matrix into an output matrix based on an inverse transformation matrix.

Abstract: In an example, a deep learning network is used to calculate a similarity score between a first query in a social networking service and each of one or more suggestable entities in the social networking service. The suggestable entities are determined via a first machine learned model. The deep learning network takes as input the suggestable entities as well as a history of interactions with a graphical user interface of a social networking service by a first member of the social networking service, a history of queries performed via the graphical user interface by the first member, and the first query itself.

Abstract: The present disclosure provides a processing device and method. The device includes: an input/output module, a controller module, a computing module, and a storage module. The input/output module is configured to store and transmit input and output data; the controller module is configured to decode a computation instruction into a control signal to control other modules to perform operation; the computing module is configured to perform four arithmetic operation, logical operation, shift operation, and complement operation on data; and the storage module is configured to temporarily store instructions and data. The present disclosure can execute a composite scalar instruction accurately and efficiently.