A method that includes receiving a string from a remote station, the string associated with an interaction event between a user and the remote station, is provided. The method includes verifying that the string includes a content validation for a user account in a network service, mapping at least a portion of the string to a ticker symbol associated with an entry in a database and transmitting, to the user, a message indicating that a fractional value associated with the ticker symbol has been added to the user account in the network service. A system configured to execute the above method is also provided.

Described herein are a device and a method for performing a network analysis. In one aspect, the device includes a reconfigurable neural network circuit to determine an indication of a predicted network characteristic. In one aspect, the reconfigurable neural network circuit includes a control circuit to select a packet attribute or a flow attribute of a raw packet stream from a pipeline, and determine a configuration setting corresponding to the packet attribute or the flow attribute. The configuration setting may indicate a configuration of the reconfigurable neural network circuit to implement a neural network. In one aspect, the reconfigurable neural network circuit includes a storage to provide neural network parameters of the neural network, according to the configuration setting. In one aspect, the reconfigurable neural network circuit includes computational circuits to perform computations based on the neural network parameters from the storage to determine the indication of the predicted network characteristic.

Described herein are a device and a method for performing a network analysis. In one aspect, the device includes a reconfigurable neural network circuit to determine an indication of a predicted network characteristic. In one aspect, the reconfigurable neural network circuit includes a control circuit to select a packet attribute or a flow attribute of a raw packet stream from a pipeline, and determine a configuration setting corresponding to the packet attribute or the flow attribute. The configuration setting may indicate a configuration of the reconfigurable neural network circuit to implement a neural network. In one aspect, the reconfigurable neural network circuit includes a storage to provide neural network parameters of the neural network, according to the configuration setting. In one aspect, the reconfigurable neural network circuit includes computational circuits to perform computations based on the neural network parameters from the storage to determine the indication of the predicted network characteristic.

This disclosure relates to implementing a configuration portfolio having a compact set of model configurations that are predicted to perform well with respect to a wide variety of input tasks. Systems described herein involve evaluating machine learning models with respect to a set of training tasks to generate a regret matrix based on accuracy of the machine learning models in connection with predicting outputs for the training tasks. The systems described herein can identify a subset of model configurations from a plurality of model configurations based on the subset of model configurations having lower associated metrics of regret with respect to the training tasks. This ensures that each model configuration within the configuration portfolio will perform reasonably well for a given input task and provides a mechanism for selecting an output model configuration using significantly fewer processing resources than conventional model selection systems.

In a method of optimizing a neural network model, first model information about a first neural network model is received. Device information about a first target device that is used to execute the first neural network model is received. An analysis whether the first neural network model is suitable for executing on the first target device is performed, based on the first model information, the device information, and at least one of a plurality of suitability determination algorithms. A result of the analysis is output such that the first model information and the result of the analysis are displayed on a screen.

Systems and methods for programming field programmable gate array (FPGA) devices are provided. A trained model for a deep learning process is obtained and converted to design abstraction (DA) code defining logic block circuits for programming an FPGA device. Each of these logic block circuits represents one of a plurality of modules that executes a processing step between different layers of the deep learning process.

A generation function to generate and output generated data from an input, a discrimination function to cause each discriminator to discriminate whether the data to be discriminated is based on the training data or the generated data and to output a discrimination result. Also an update function to update the discriminator that has output the discrimination result such that the data to be discriminated is discriminated with higher accuracy, and to further update the generator to increase a probability of discriminating that the generated data-based data to be discriminated is the training data-based data, and a whole update function to cause the updates to be executed for the generator and all the discriminators.

Disclosed are a scheduling method and a related apparatus. A computing apparatus in a server can be chosen to implement a computation request, thereby improving the running efficiency of the server.

Disclosed are a scheduling method and a related apparatus. A computing apparatus in a server can be chosen to implement a computation request, thereby improving the running efficiency of the server.

A deep learning heterogeneous computing method based on layer-wide memory allocation, at least comprises steps of: traversing a neural network model so as to acquire a training operational sequence and a number of layers L thereof; calculating a memory room R.sub.1 required by data involved in operation at the i.sup.th layer of the neural network model under a double-buffer configuration, where 1≤i≤L; altering a layer structure of the i.sup.th layer and updating the training operational sequence; distributing all the data across a memory room of the CPU and the memory room of the GPU according to a data placement method; performing iterative computation at each said layer successively based on the training operational sequence so as to complete neural network training.