10 results about "Network Computing System" patented technology

The application discloses a kind of diffractive light neural network computing systems and methods for realizing pure optical nonlinearity, belong to photonic computing and artificial intelligence hardware technical field.Its system includes coherent light source, linear light computing unit, nonlinear light activation unit and optical detection unit arranged in order along optical path, wherein linear light computing unit is programmable phase modulation using spatial light modulator, nonlinear light activation unit is programmable binary amplitude modulation using digital micromirror device, and both are directly coupled to form pure optical computing path without photoelectric conversion on optical path.Its method trains neural network through customized loss function, drives nonlinear activation function output to binary convergence, and maps the parameters obtained by training into phase map and binary mask respectively and loads to hardware.The application realizes true all-optical nonlinear computation, with the advantages of high energy efficiency, extremely low delay, compact structure and strong parallel processing capability.

A system includes a network computing system and a user computing device. The network computing system includes a plurality of software services associated with aircraft operations. The user computing device is operable to connect to the network computing system, over a network, to access data from one or more software services of the plurality of software services. The user computing device is operable to automatically perform one or more pre-flight computing functions for an aircraft based on the data from the one or more software services of the network computing system. At least one of the pre-flight computing functions includes an automatic weight and balance computation for a payload of the aircraft based on the data accessed from the one or more software services of the network computing system.

A computing system is disclosed in which a tensor-network structure mutates autonomously during execution. A structural mutation unit modifies tensor-network topology concur- rently with tensor contraction execution without halting computation or requiring host processor intervention.

A method of a computing system of a communication network for providing video to a portable display device includes establishing a connection with a receiving system comprising the portable display device, obtaining compressed video data and decoding the compressed video data, receiving direction information from the receiving system, the direction information related to a direction of the portable display device, identifying a respective location of each macroblock of a plurality of macroblocks of the decoded video data, determining a respective distance between a location determined based on a field of view identified utilizing the direction information, and the respective location of each macroblock of the decoded video data, encoding the decoded video data utilizing a respective quantization parameter identified based on the respective distance determined for each macroblock to provide re-encoded video data, and sending the re-encoded video data to the receiving system for display on the portable display device.

A network computing system receives, over one or more networks, a service request from a requester device. The network computer system predicts a processing time interval, where the processing time extends from a time when the service request is received until a time when a service provider that is selected by the network computer system accepts an invitation to fulfill the service request. The network computer system transmits content data to the requester device, where the content data causes the requester device to display content that is based on the predicted processing time interval.

The application discloses a kind of Sub-GHz-based airborne wireless sensor network node system and matching network calculation method, including sensor sensitive unit module, single-chip microcomputer control module, wireless communication module and power supply module;With the compatibility between the original equipment of Sub-GHz-based wireless communication module design ensures airborne, optimize impedance matching circuit makes transmission power reach 20dBM, center frequency 915MHz, bandwidth 530MHz, at the suppression of secondary harmonic frequency and third harmonic frequency reaches-40dB below, wireless module maximum transmission rate can reach 2Mbps, and through electromagnetic compatibility test ensures the anti-interference of wireless communication module.The application has strong anti-interference ability, signal attenuation is slow, and low power consumption is suitable for solving cabin complex environment.

The application discloses a kind of multi-layer nonlinear optical neural network computing systems based on linear signal re-encoding, belong to optical neural network computing technical field, including: control circuit and optical processing chip system;The control circuit is used to realize the loading of input signal and the reading of output result;The optical processing chip system includes light source, integrated optical computing chip and detector, and the light carrier emitted by light source generates input optical signal after being modulated by input signal, and after being processed by multi-layer nonlinear activation in linear calculation area, linear re-encoding area and high-speed modulation area on integrated optical computing chip, output result is read by detector and returned to control circuit.The application utilizes pure linear device to realize all-optical nonlinear network, cooperates analog activation function by re-encoding and modulation, needs only single physical layer to construct depth architecture without photoelectric conversion, with the advantages of all-optical processing, CMOS compatible, high speed and low power consumption.

The invention discloses a tensor processing method used in a neural network computing system. The tensor processing method comprises the following steps: creating a computational graph descriptor for each tensor in the neural network computing system; when a source tensor needs to be copied or a band is copied to a target tensor, acquiring a read computational graph of the source tensor from a computational graph descriptor of the source tensor, and acquiring a write computational graph of the target tensor from a computational graph descriptor of the target tensor; splicing the output end of the reading calculation graph of the source tensor and the input end of the writing calculation graph of the target tensor to form a complete carrying calculation graph, and performing optimization operation on the carrying calculation graph; and generating execution code for the target hardware according to the optimized transport computational graph.

A system includes a network computing system and a user computing device. The network computing system includes a plurality of software services associated with aircraft operations. The user computing device is operable to connect to the network computing system, over a network, to access data from one or more software services of the plurality of software services. The user computing device is operable to automatically perform one or more pre-flight computing functions for an aircraft based on the data from the one or more software services of the network computing system. At least one of the pre-flight computing functions includes an automatic weight and balance computation for a payload of the aircraft based on the data accessed from the one or more software services of the network computing system.

In some embodiments, a server system may re-allocate a resource to a node from a shared pool of available resources and reschedule the added allocation. Some embodiments may use such operations to avoid triggering application-terminating thresholds and increase the efficiency and consistency of a networked computing system. In response to obtaining a result indicating whether an action will cause a resource allocation level of a node to fail to satisfy a threshold, some embodiments may delay the action by a buffer duration and allocate a set of resources from a resource pool to the node. As such, in some embodiments, the set of resources may be available to the node before an end of the buffer duration. Thus, at the end of the buffer duration or after the buffer duration, some embodiments may execute the action without triggering a failure condition related to the node.