A computing core circuit, including: an encoding module, a route sending module, and a control module, wherein the control module is configured to control the encoding module to perform encoding processing on a pulse sequence determined by pulses to be transmitted of at least one neuron in a current computing core, so as to obtain an encoded pulse sequence, and control the route sending module to determine a corresponding route packet according to the encoded pulse sequence, so as to send the route packet. The present disclosure further provides a data processing method, a chip, a board, an electronic device, and a computer-readable storage medium.

A method for training a deep neural network according to an embodiment includes training a deep neural network model using a first data set including a plurality of labeled data and a second data set including a plurality of unlabeled data, assigning a ground-truth label value to some of the plurality of unlabeled data, updating the first data set and the second data set such that the data to which the ground-truth label value is assigned is included in the first data set, and further training the deep neural network model using the updated first data set and the updated second data set.

An information handling system may include at least one processor; and a non-transitory memory coupled to the at least one processor. The information handling system may be configured to: receive a command associated with an Intelligent Platform Management Interface (IPMI) protocol; package the command for transmission according to a Redfish protocol; and transmit the packaged command to a Redfish interface of a management controller, wherein the management controller is configured to unpackage and execute the command.

A system and method for generating a context block using system parameters. The system parameters include objective parameters, functionality parameters, and interface definitions. Context field definitions are received. The system parameters and context fields definitions may be used to determine context fields and context entries. The system parameters may be used to determine context fields and number of context entries. The context module hardware description may be created using context fields, number of context entries, and context field definitions.

Embodiments described herein are generally directed to a local cache structure within a shared function of a 3D pipeline that facilitates efficient caching of resource state. In an example, the cache structure is maintained within a sub-core of a GPU. The local cache structure includes (i) an SC having entries each containing a state of a binded resource, and (ii) a DSAT having entries each containing an index into the SC. The DSAT is tagged by SBTO values representing addresses of entries of a binding table. A request, including information indicative of an SBTO pointing to an entry within the binding table, is received for a state of a particular binded resource being accessed by a shared function of the 3D pipeline. Based on the SBTO and during a single access to the cache structure, a determination is made regarding whether the state of the particular binded resource is present.

Computing platforms, methods, and storage media for processing a data request are disclosed. Exemplary implementations may: receive a data transfer request including data transfer instructions associated with the data transfer; obtain an aggregator access profile specifying application programming interface (API) access to be granted to a data aggregator with respect to one or more APIs; obtain a user account authorization token specifying access to be granted to the data aggregator with respect to one or more user accounts; determine access permissions associated with the data transfer instructions based on a combination of the aggregator access profile and the user account authorization token; and transmit an access notification based on the determined access permissions, the access notification specifying the combination of the one or more APIs and the one or more user accounts to which the data aggregator is granted access. Application-specific account permissions and data type permissions may be specified.

A system and method on one computer records actions on objects performed on a second computer. If an object is deleted from the second computer, the first computer can restore to a point in time the state of the object on the second computer, including assignments made between it and other objects, using the recorded actions.

A system and method on one computer records actions on objects performed on a second computer. If an object is deleted from the second computer, the first computer can restore to a point in time the state of the object on the second computer, including assignments made between it and other objects, using the recorded actions.

A pre-boot screen can be accessed remotely. A remote access BIOS module can be employed on a computing system to logically split a GPU into a local screen rendering part and a remote access part and to logically split GPU memory into a local screen memory region and a remote access memory region. The remote access part of the GPU can access a pre-boot screen that has been copied to the remote access memory region and create network packets with payloads containing the pre-boot screen. The remote access part can then cause the network packets to be transferred to a NIC for delivery to a remote computing system. A remote access application on the remote computing system can extract and display the pre-boot screen. The remote access application can relay any user input back to the remote access BIOS module which can then cause the user input to be implemented locally.

The present disclosure describes methods, systems, and computer program products for data-centric integration modeling in an application integration system. One computer-implemented method includes receiving, by operation of an integration system, a logic integration program comprising a plurality of logic integration patterns that are defined in a data-centric logic integration language; generating a logical model graph based on the logic integration program, the logical model graph being runtime-independent; converting the logical model graph into a physical model graph, the physical model graph being runtime-specific; and generating logic integration runtime codes executable by the integration system based on the physical model graph.