Disclosed herein are a convolutional layer acceleration unit, an embedded system having the convolutional layer acceleration unit, and a method for operating the embedded system. The method for operating an embedded system, the embedded system performing an accelerated processing capability programmed using a Lightweight Intelligent Software Framework (LISF), includes initializing and configuring, by a parallelization managing function entity (FE), entities present in resources for performing mathematical operations in parallel, and processing in parallel, by an acceleration managing FE, the mathematical operations using the configured entities.

A parallel processing unit (PPU) can be divided into partitions. Each partition is configured to operate similarly to how the entire PPU operates. A given partition includes a subset of the computational and memory resources associated with the entire PPU. Software that executes on a CPU partitions the PPU for an admin user. A guest user is assigned to a partition and can perform processing tasks within that partition in isolation from any other guest users assigned to any other partitions. Because the PPU can be divided into isolated partitions, multiple CPU processes can efficiently utilize PPU resources.

In one embodiment, a processor includes a memory hierarchy and a core coupled to the memory hierarchy. The memory hierarchy stores encrypted data, and the core includes circuitry to access the encrypted data stored in the memory hierarchy, decrypt the encrypted data to yield decrypted data, perform an entropy test on the decrypted data, and update a processor state based on a result of the entropy test. The entropy test may include determining a number of data entities in the decrypted data whose values are equal to one another, determining a number of adjacent data entities in the decrypted data whose values are equal to one another, determining a number of data entities in the decrypted data whose values are equal to at least one special value from a set of special values, or determining a sum of n highest data entity value frequencies.

A method for containerized workload scheduling can include determining a network state for a first hypervisor in a virtual computing cluster (VCC). The method can further include determining a network state for a second hypervisor. Containerized workload scheduling can further include deploying a container to run a containerized workload on a virtual computing instance (VCI) deployed on the first hypervisor or the second hypervisor based, at least in part, on the determined network state for the first hypervisor and the second hypervisor.

In some embodiments, a real-time event is detected and context is determined based on the real-time event. An application model is fetched based on the context and meta-data associated with the real-time event, the application model referencing a micro-function and including pre-condition and post-condition descriptors. A graph is constructed based on the micro-function. The micro-function is transformed into micro-capabilities by determining a computing resource for execution of a micro-capability by matching pre-conditions and post-conditions of the micro-capability, and enabling execution and configuration of the micro-capability on the computing resource by providing access in a target environment to an API capable of calling the micro-capability to configure and execute the micro-capability. A request is received from the target environment to execute and configure the micro-capability on the computing resource. The micro-capability is executed and configured on the computing resource, and an output of the micro-capability is provided to the target environment.

In one embodiment, a processor includes: a plurality of cores each comprising a multi-threaded core to concurrently execute a plurality of threads; and a control circuit to concurrently enable at least one of the plurality of cores to operate in a single-threaded mode and at least one other of the plurality of cores to operate in a multi-threaded mode. Other embodiments are described and claimed.

Task delegation and cooperation for automated assistants is presented. A method comprises receiving, at a centralized support center that is in contact with a plurality of automated assistants including a first automated assistant and a second automated assistant, a request to perform a task on behalf of an individual, formulating, at the centralized support center, the task as a plurality of sub-tasks including a first sub-task and a second sub-task, delegating, at the centralized support center, the first sub-task to the first automated assistant, based on a determination at the centralized support center that the first automated assistant is capable of performing the first sub-task, and delegating, at the centralized support center, the second sub-task to the second automated assistant, based on a determination at the centralized support center that the second automated assistant is capable of performing the second sub-task.

Disclosed is a system and method for cloud workload provisioning. In one implementation, the present invention provides a system enabling an automated guidance to the user for the workload to be provisioned. The present invention matches the user's workload profile based on a wide variety of historical data set and makes easy for users to choose the cloud provisioning for various kinds of workloads. The system can automatically readjust a workload profile for cloud provisioning. The system can provide a manual selection option for cloud provisioning. In one embodiment, the present invention provides a system and method that derives a workload provision scaling factor mechanism using historic data set. Furthermore, the system and method can automatically or manually readjust the provision scaling factor based on a workload profile for cloud provisioning.

Embodiments of a software defined automation system that provides a reference architecture for designing, managing and maintaining a highly available, scalable and flexible automation system. In some embodiments, an SDA system can include a localized subsystem including a system controller node and multiple compute nodes. The multiple compute nodes can be communicatively coupled to the system controller node via a first communication network. The system controller node can manage the multiple compute nodes and virtualization of a control system on a compute node via the first communication network. The virtualized control system includes virtualized control system elements connected to a virtual network that is connected to a second communication network to enable the virtualized control system elements to control a physical control system element via the second communication network connected to the virtual network.

Embodiments of a software defined automation system that provides a reference architecture for designing, managing and maintaining a highly available, scalable and flexible automation system. In some embodiments, an SDA system can include a localized subsystem including a system controller node and multiple compute nodes. The multiple compute nodes can be communicatively coupled to the system controller node via a first communication network. The system controller node can manage the multiple compute nodes and virtualization of a control system on a compute node via the first communication network. The virtualized control system includes virtualized control system elements connected to a virtual network that is connected to a second communication network to enable the virtualized control system elements to control a physical control system element via the second communication network connected to the virtual network.