Abstract: Systems, apparatuses and methods may provide for source device technology that identifies a plurality of object regions in a video frame, automatically generates context information for the video frame on a per-object region basis and embeds the context information in a signal containing the video frame. Additionally, playback device technology may decode a signal containing a video frame and embedded context information, identifies a plurality of object regions in the video frame based on the embedded context information, and automatically selects one or more post-processing configurations for the video frame on a per-object region basis.

Abstract: Systems, apparatuses and methods may provide for technology that detects a tensor operation in an application, wherein the tensor operation has an unspecified tensor input size, determines the input tensor size at runtime, and selects a partition configuration for the tensor operation based at least in part on the input tensor size and one or more runtime conditions. In one example, the technology searches a lookup table for the input tensor size and at least one of the runtime condition(s) to select the partition configuration.

Abstract: Systems, apparatuses and methods may provide for technology that detects a generic cloud service call in an application, wherein platform-specific parameters are unspecified in the cloud service call. The technology may also select a first cloud platform based on one or more performance constraints associated with the first cloud platform and automatically generate a first platform-specific service call based on the cloud service call and the first set of parameters. In one example, the technology also maps the cloud service call to the first platform-specific service call. Additionally, the technology may migrate the cloud service call to a second cloud platform without rewriting the generic cloud service call.

Abstract: Embodiments of systems, apparatuses and methods provide enhanced function as a service (FaaS) to users, e.g., computer developers and cloud service providers (CSPs). A computing system configured to provide such enhanced FaaS service include one or more controls architectural subsystems, software and orchestration subsystems, network and storage subsystems, and security subsystems. The computing system executes functions in response to events triggered by the users in an execution environment provided by the architectural subsystems, which represent an abstraction of execution management and shield the users from the burden of managing the execution. The software and orchestration subsystems allocate computing resources for the function execution by intelligently spinning up and down containers for function code with decreased instantiation latency and increased execution scalability while maintaining secured execution.

Abstract: Systems, apparatuses and methods may provide for technology that generates a first compiler output based on input code that includes dynamically typed variable information and generates a second compiler output based on the input code, wherein the second compiler output includes type check code to verify one or more type inferences associated with the first compiler output. The technology may also execute the first compiler output and the second compiler output in parallel via different threads.

Abstract: Systems, apparatuses and methods may provide for technology that sends a first message via an input output (IO) link, wherein the first message includes a first rendering asset and an identifier (ID) associated with the first rendering asset. The technology may also exclude a second rendering asset from a second message in response to the ID being shared by the first rendering asset and the second rendering asset and send the second message via the IO link, wherein the second message includes the ID. In one example, the ID is a hash ID.

Abstract: Systems, apparatuses and methods may provide for technology that detects a tensor operation in an application, wherein the tensor operation has an unspecified tensor input size, determines the input tensor size at runtime, and selects a partition configuration for the tensor operation based at least in part on the input tensor size and one or more runtime conditions. In one example, the technology searches a lookup table for the input tensor size and at least one of the runtime condition(s) to select the partition configuration.

Abstract: Systems, apparatuses and methods may provide for technology that generates inclusion data in accordance with a Poisson distribution, wherein the inclusion data specifies a number of inclusions for each observation in a set of observations. The technology may also train a first decision tree in a random forest based at least in part on the inclusion data.

Abstract: Systems, apparatuses and methods may provide for source device technology that identifies a plurality of object regions in a video frame, automatically generates context information for the video frame on a per-object region basis and embeds the context information in a signal containing the video frame. Additionally, playback device technology may decode a signal containing a video frame and embedded context information, identifies a plurality of object regions in the video frame based on the embedded context information, and automatically selects one or more post-processing configurations for the video frame on a per-object region basis.

Abstract: Systems, apparatuses and methods may provide for technology that detects a tensor operation in an application, wherein the tensor operation has an unspecified tensor input size, determines the input tensor size at runtime, and selects a partition configuration for the tensor operation based at least in part on the input tensor size and one or more runtime conditions. In one example, the technology searches a lookup table for the input tensor size and at least one of the runtime condition(s) to select the partition configuration.

Abstract: Systems, apparatuses and methods may provide for technology that identifies a prioritization data structure associated with a function, wherein the prioritization data structure lists hardware resource types in priority order. The technology may also allocate a first type of hardware resource to the function if the first type of hardware resource is available, wherein the first type of hardware resource has a highest priority in the prioritization data structure. Additionally, the technology may allocate, in the priority order, a second type of hardware resource to the function if the first type of hardware resource is not available.

Abstract: Systems, apparatuses and methods may provide technology for managing a runtime computing environment having tiered object memory placement that assigns a hotness score to an object having an object type based on an invocation count of objects referenced by a hot method, allocates a newly-created object to one of a hot object heap, said hot object heap assigned to store hot objects in a first memory tier, or a cold object heap, said cold object heap assigned to store cold objects in a second memory tier, based on the hotness score associated with the object type for the newly-created object, and migrates a plurality of objects between the hot object heap and the cold object heap based on a hotness score associated with each object. The technology may also operate the object migration in an execution thread independent of an execution thread for the object allocation.

Abstract: Method, systems and apparatuses may include technology that identifies a second computing platform that includes a second hardware device that satisfies one or more conditions. The second hardware device is associated with a hardware abstraction layer on the second computing platform. The second computing platform is coupled to a first computing platform. The technology may further include generating a virtual hardware abstraction layer that is to represent the hardware abstraction layer on the second computing platform.

Abstract: Systems, apparatuses and methods may provide for technology that generates a first compiler output based on input code that includes dynamically typed variable information and generates a second compiler output based on the input code, wherein the second compiler output includes type check code to verify one or more type inferences associated with the first compiler output. The technology may also execute the first compiler output and the second compiler output in parallel via different threads.

Abstract: Systems, apparatuses and methods may provide for technology that sends a first message via an input output (IO) link, wherein the first message includes a first rendering asset and an identifier (ID) associated with the first rendering asset. The technology may also exclude a second rendering asset from a second message in response to the ID being shared by the first rendering asset and the second rendering asset and send the second message via the IO link, wherein the second message includes the ID. In one example, the ID is a hash ID.

Abstract: Systems, apparatuses and methods may provide for technology that detects a runtime call to a communication library, wherein the runtime call identifies a memory buffer, determines that a class of service (CLOS) attribute is associated with the memory buffer, and issues a driver instruction to modify the CLOS attribute in response to the runtime call.

Abstract: Embodiments of systems, apparatuses and methods provide enhanced function as a service (FaaS) to users, e.g., computer developers and cloud service providers (CSPs). A computing system configured to provide such enhanced FaaS service include one or more controls architectural subsystems, software and orchestration subsystems, network and storage subsystems, and security subsystems. The computing system executes functions in response to events triggered by the users in an execution environment provided by the architectural subsystems, which represent an abstraction of execution management and shield the users from the burden of managing the execution. The software and orchestration subsystems allocate computing resources for the function execution by intelligently spinning up and down containers for function code with decreased instantiation latency and increased execution scalability while maintaining secured execution.

Abstract: Systems, apparatuses and methods may provide for technology that identifies a prioritization data structure associated with a function, wherein the prioritization data structure lists hardware resource types in priority order. The technology may also allocate a first type of hardware resource to the function if the first type of hardware resource is available, wherein the first type of hardware resource has a highest priority in the prioritization data structure. Additionally, the technology may allocate, in the priority order, a second type of hardware resource to the function if the first type of hardware resource is not available.

Abstract: Systems, apparatuses and methods may provide for technology that detects a generic cloud service call in an application, wherein platform-specific parameters are unspecified in the cloud service call. The technology may also select a first cloud platform based on one or more performance constraints associated with the first cloud platform and automatically generate a first platform-specific service call based on the cloud service call and the first set of parameters. In one example, the technology also maps the cloud service call to the first platform-specific service call. Additionally, the technology may migrate the cloud service call to a second cloud platform without rewriting the generic cloud service call.

Abstract: Systems, apparatuses and methods may provide for technology that detects a tensor operation in an application, wherein the tensor operation has an unspecified tensor input size, determines the input tensor size at runtime, and selects a partition configuration for the tensor operation based at least in part on the input tensor size and one or more runtime conditions. In one example, the technology searches a lookup table for the input tensor size and at least one of the runtime condition(s) to select the partition configuration.