89 results about "Dataflow computation" patented technology

Automated decision making techniques are provided. For example, a technique for generating a decision associated with an individual or an entity includes the following steps. First, two or more data streams associated with the individual or the entity are captured. Then, at least one time-varying measure is computed in accordance with the two or more data streams. Lastly, a decision is computed based on the at least one time-varying measure. One form of the time-varying measure may include a measure of the coverage of a model associated with previously-obtained training data by at least a portion of the captured data. Another form of the time-varying measure may include a measure of the stability of at least a portion of the captured data. While either measure may be employed alone to compute a decision, preferably both the coverage and stability measures are employed. The technique may be used to authenticate a speaker.

For communication networks comprising user devices, edge routers, core routers, access and core links, a specification is given for a novel method and apparatus computing and allocating fair transmission rates to user data flows from a plurality of quality of service levels. The fair rates satisfy the minimum transmission rates, the end-to-end delays and the data loss rates required by each flow and also avoid network congestion. The method comprises: an edge router process and a flow control shaper for each edge router and a core router process for each edge and core router. All processes are executed in a distributed and asynchronous manner, are stable and converge to the desired fair rates. Each flow shaper process shapes the transmission rates based on local measurements driving them to the desired fair rates. The processes are efficient and lend themselves into ASIC and network processor unit implementations.

The invention discloses a multi-core tensor processor of a neural network. The multi-core tensor processor comprises a main controller, a reconstruction controller and a plurality of data flow calculation engines, the main controller is used for providing a control and state interface of the neural network tensor processor for an external control unit, and providing first configuration informationand a first initial signal for the reconstruction controller; the reconstruction controller receives the first configuration information and the first starting signal, obtains a reconstruction instruction of an external memory after the first starting signal is valid, and analyzes the reconstruction instruction to generate multiple groups of second configuration information and second starting signals; and each data flow calculation engine respectively receives the corresponding second configuration information and the second initial signal, performs function configuration according to the second configuration information, acquires data and parameters of the external memory to execute operation after the second initial signal is valid, and writes a calculation result into the external memory. The multi-core tensor processor is suitable for performing centralized calculation on a neural network algorithm, and has universality and expandability.

The invention provides a user portrait data monitoring method, system and device and a storage medium. The user portrait data monitoring method comprises the following steps: collecting user portraitdata from each node in a user portrait data link; storing the user portrait data in a message queue; performing real-time statistics on the user portrait data in the message queue by adopting a data flow calculation engine; and storing a statistical result of the data flow calculation engine in a statistical database. By adopting the scheme of the invention, full-link monitoring is carried out onthe user portrait data link, and the data statistics efficiency is improved by combining the message queue and the data flow calculation engine, so that the data of each node in the user portrait datalink can be captured and analyzed in time, and problems can be found in time.

The invention provides an SDN distributed control unmanned aerial vehicle network flow configuration method and a system thereof. According to the data flow information, the original node and the target node of the new path are obtained, and the local network topology table is transformed into a directed graph with weights. According to the link weights in the directed graph, the original node andthe target node are used to calculate the new path for the data flow. Sending a MOD message to a UAV node controller on the new path according to the data stream information priority. The UAV node controller that receives the MOD message replies to the corresponding ACK message, completes the route switching, and realizes the traffic configuration. In order to solve the problem of communication interruption caused by UAV leaving the network due to power exhaustion in the network, by distinguishing the priority of data stream, the high-level users can finish the traffic planning first to realize the communication.

The invention discloses a vehicle data processing method. The method is applied to a vehicle terminal, and the vehicle terminal is connected with a vehicle through an OBD interface. The method comprises the following steps: the vehicle terminal reads a first PID list supported by the vehicle after the vehicle starts, uploads the first PID list to an assigned mobile terminal convenient for a user to select the needed PID, receives a second PID list fed back by the mobile terminal, and reads PID dataflow corresponding to PIDs in the second PID list, values corresponding to all the PIDs in the second PID list are calculated according to the read PID dataflow every preset time, and the values obtained after calculation are uploaded to the mobile terminal and are displayed by the mobile terminal. The invention also discloses the vehicle terminal. The vehicle terminal effectively realizes users' customized settings, meets users' user demands, and improves users' use experiences.

The invention brings forward a real-time grading method for credit of users. The method comprises the following steps: a data acquisition step of acquiring basic data of users through internet, a dataprocessing step of importing acquired basic data into dataflow computation cluster for real-time data processing, a grading step of importing processed basic data into one or multiple grading modelsand grading the processed basic data, wherein one or multiple grading models is / are established according to existing data, a data storage and feedback step of saving the basic data, the processed basic data and grades into a database and acquiring feedback information, a model evaluation and optimization step of evaluating and optimizing the one or multiple grading models and the dataflow computation cluster according to the saved basic data, the processed basic data, the grades and the feedback information, and an update step of updating the applied one or multiple grading models and the dataflow computation cluster according to optimized one or multiple grading models and the dataflow computation cluster. The invention also puts forward a real-time grading system for credit of users.

A system and method for implementing a unified model for integration systems is presented. A user provides inputs to an integrated language engine for placing operator components and arc components onto a dataflow diagram. Operator components include data ports for expressing data flow, and also include meta-ports for expressing control flow. Arc components connect operator components together for data and control information to flow between the operator components. The dataflow diagram is a directed acyclic graph that expresses an application without including artificial boundaries during the application design process. Once the integrated language engine generates the dataflow diagram, the integrated language engine compiles the dataflow diagram to generated application code.

Methods and systems are described for sequentially obtaining a plurality of data streams, the plurality of data streams comprising a data stream in a current condition, a data stream in a skewed-forward condition, and a data stream in a skewed-backward condition, calculating, for each data stream in the plurality of data streams, a corresponding set of cost-function values by obtaining a corresponding set of eye measurements, the eye measurements obtained by adjusting a sampling threshold of a sampler generating a plurality of samples of the data stream, the plurality of samples comprising edge samples and data samples, wherein the data stream is sampled at a rate equal to twice a rate of the data stream and calculating the corresponding set of cost-function values based on the corresponding set of eye measurements, and generating a skew control signal based on a comparison of the sets of calculated cost-function values.

The disclosure relates in some aspects to protecting systems and data from maliciously caused destruction. Data integrity is maintained by monitoring data to detect and prevent potential attacks. A mechanism for tracking whether data is tainted is implemented in a Data Flow computer architecture or some other suitable architecture. In some aspects, a taint checking mechanism is implemented within a register file, memory management, and an instruction set of such an architecture. To this end, an indication of whether the data stored in a given physical memory location is tainted is stored along with the physical memory location. For example, a register can include a bit for a corresponding taint flag, a memory page can include a bit for a corresponding taint flag, and an input / output (I / O) port can include a bit for a corresponding taint flag.

Systems and methods for improving the performance of a MIMO wireless communication system by reducing the amount of uplink resources that are needed to provide channel performance feedback for the adjustment of data rates on the downlink MIMO channels. In one embodiment, a method comprises encoding each of a set of data streams according to corresponding data rates, permuting the data streams on a set of MIMO channels according to a full permutation of combinations, transmitting the permuted data streams, receiving the permuted data streams, decoding and determining an SNR for each of the data streams, computing a condensed SNR metric for the set of data streams, providing the condensed metric as feedback, determining a set of individual SNR metrics for the data streams based on the condensed SNR metric, and adjusting the data rates at which the data streams are encoded based on the individual SNR metrics.

Aggregates are calculated from a data stream in which data is sent in a sequence of tuples, in which each tuple comprises an item identifier and a timestamp indicating when the tuple was transmitted. The tuples may arrive out-of-order, that is, the sequence in which the tuples arrive are not necessarily in the sequence of their corresponding timestamps. In calculating aggregates, more recent data may be given more weight by multiplying each tuple by a decay function which is a function of the timestamp associated with the tuple and the current time. The tuples are recorded in a quantile-digest data structure. Aggregates are calculated from the data stored in the quantile-digest data structure.