Techniques are disclosed for scheduling artificial intelligence model partitions for execution in an information processing system. For example, a method comprises the following steps. An intermediate representation of an artificial intelligence model is obtained. A reversed computation graph corresponding to a computation graph generated based on the intermediate representation is obtained. Nodes in the reversed computation graph represent functions related to the artificial intelligence model, and one or more directed edges in the reversed computation graph represent one or more dependencies between the functions. The reversed computation graph is partitioned into sequential partitions, such that the partitions are executed sequentially and functions corresponding to nodes in each partition are executed in parallel.

Techniques for improved mobile application architectures and service communication protocols are discussed herein. Some embodiments may include a mobile device configured for providing a mobile application including multiple service applications. The service applications may execute asynchronously and in separate containers, providing service orientated architecture (SOA)-like services with respect to other portions of the mobile application, or even external applications. The separation of a monolithic mobile application into separate service applications provide advantages in terms of application performance, development, and maintenance. For example, a subset of all service applications may be started up, and executed on demand to improve device resource utilization efficiency.

A method performed by a terminal in a wireless communication system includes: receiving, from a base station, configuration information associated with a channel state information (CSI) report including at least one information associated with a CSI resource setting; receiving, from the base station, information associated with channel occupancy duration; determining whether at least one symbol for receiving a channel state information reference signal (CSI-RS) is within channel occupancy duration, based on the at least one information associated with the CSI resource setting and the received information associated with the channel occupancy duration; receiving, from the base station, at least one CSI-RS on the at least one symbol, based on a result of the determining; and when the CSI report is determined to be transmitted, transmitting; to the base station, the CSI report based on the configuration information associated with the CSI report and the received at least one CSI-RS.

Techniques for optimizing CPU usage in a host system based on VM guest OS power and performance management are provided. In one embodiment, a hypervisor of the host system can capture information from a VM guest OS that pertains to a target power or performance state set by the guest OS for a vCPU of the VM. The hypervisor can then perform, based on the captured information, one or more actions that align usage of host CPU resources by the vCPU with the target power or performance state.

An apparatus comprises an interrupt distributor to distribute virtual interrupts to one or more physical processors, each virtual interrupt to be handled by one of a plurality of virtual processors mappable to said one or more physical processors; and control circuitry to maintain virtual processor interrupt tracking information corresponding to a given virtual processor. The virtual processor interrupt tracking information includes a pending interrupt record tracking which types of virtual interrupts are pending for the given virtual processor, and separate from the pending interrupt record, a pending interrupt status indication indicating a pending interrupt status for the given virtual processor. The pending interrupt status indicates whether the number of pending virtual interrupts for the given virtual processor is zero.

Various embodiments, methods and systems for implementing a distributed computing system machine-learning training service are provided. Initially a machine learning model is accessed. A plurality of synthetic data assets are accessed, where a synthetic data asset is associated with asset-variation parameters that are programmable for machine-learning. The machine learning model is retrained using the plurality of synthetic data assets. The machine-learning training service is further configured for executing real-time calls to generate an on-the-fly-generated synthetic data asset such that the on-the-fly-generated synthetic data asset is rendered in real-time to preclude pre-rendering and storing the on-the-fly-generated synthetic data asset. The machine-learning training service further supports hybrid-based machine learning training, where the machine learning model is trained based on a combination of the plurality of synthetic data assets, a plurality of non-synthetic data assets, and synthetic data asset metadata associated with the plurality of synthetic data assets.

Representative apparatus, method, and system embodiments are disclosed for a self-scheduling processor which also provides additional functionality. Representative embodiments include a self-scheduling processor, comprising: a processor core adapted to execute instructions; and a core control circuit adapted to automatically schedule an instruction for execution by the processor core in response to a received work descriptor data packet. In a representative embodiment, the processor core is further adapted to execute a non-cached load instruction to designate a general purpose register rather than a data cache for storage of data received from a memory circuit. The core control circuit is also adapted to schedule a fiber create instruction for execution by the processor core, and to generate one or more work descriptor data packets to another circuit for execution of corresponding execution threads. Event processing, data path management, system calls, memory requests, and other new instructions are also disclosed.

Examples herein relate to developing an orchestration plan. Examples disclose the development of a representation of a set of services wherein each service relates to other services via different types of relationships. The examples apply a set of dependency rules for each type of relationship at each service within the set of services such that the application of the set of dependency rules creates inter-service dependencies between state transitions of the set of services. Based on the creation of the inter-service dependencies, the orchestration plan is developed which includes a sequenced order of the state transitions for the set of services.

Systems and methods for performing parallel computation are disclosed. The system can include: a task manager; and a plurality of cores coupled with the task manager and configured to respectively perform a set of parallel computation tasks based on instructions from the task manager, wherein each of the plurality of cores further comprises: a processing unit configured to generate a first output feature map corresponding to a first computation task among the set of parallel computation tasks; an interface configured to receive one or more instructions from the task manager to collect external output feature maps corresponding to the set of parallel computation tasks from other cores of the plurality of cores; a reduction unit configured to generate a reduced feature map based on the first output feature map and received external output feature maps.

Methods, systems, and apparatus, including computer programs encoded on computer storage media for data security protection are provided. One of the methods includes: receiving a job associated with a project, wherein the project is associated with one or more data sources; identifying a plurality of inputs and a plurality of outputs associated with the job; determining a plurality of required permissions associated with the job, wherein each of the required permissions comprises an operation on a required data source, the operation corresponding to at least one of the inputs or the outputs; verifying that the one or more data sources associated with the project comprise the required data source associated with each of the required permissions; and generating a token associated with the job, the token encoding the required permissions associated with the job, wherein the token is required for execution of the job.