Abstract: A computer-implemented method for allocating memory bandwidth of multiple CPU cores in a server includes: receiving an access request to a last level cache (LLC) shared by the multiple CPU cores in the server, the access request being sent from a core with a private cache holding copies of frequently accessed data from a memory; determining whether the access request is an LLC hit or an LLC miss; and controlling a memory bandwidth controller based on the determination. The memory bandwidth controller performs a memory bandwidth throttling to control a request rate between the private cache and the last level cache. The LLC hit of the access request causes the memory bandwidth throttling initiated by the memory bandwidth controller to be disabled and the LLC miss of the access request causes the memory bandwidth throttling initiated by the memory bandwidth controller to be enabled.

Abstract: The present application discloses a computing system and an associated method. The computing system includes a memory, a master computing device and a slave computing device. The master computing device includes a memory controller and an input-output memory management unit (IOMMU). When the slave computing device accesses a first virtual address, and a first translation lookaside buffer (TLB) of the slave computing device does not store the first virtual address, the first TLB sends a translation request to the IOMMU. The IOMMU traverses page tables of the memory controller to obtain a first physical address corresponding to the first virtual address, selects and clears a first virtual address entry from a second TLB of the computing system according to a recent use time and a dependent workload of each virtual address entry to store the first virtual address and the first physical address.

Abstract: Core-aware caching systems and methods for non-inclusive non-exclusive shared caching based on core sharing behaviors of the data and/or instructions. In one implementation, the caching between a shared cache level and a core specific cache level can be based on physical page number (PPN) and core identifier sets for previous accesses to the respective physical page numbers. In another implementation, the caching between a shared cache level and a core specific cache level can be based on physical page number and core valid bit vector sets for previous accesses to the respective physical page numbers by each of the plurality of cores.

Abstract: A mechanism is described for facilitating portable, reusable, and shareable Internet of Things-based services and resources according to one embodiment. A method of embodiments, described herein, includes selecting a recipe, wherein the recipe includes instructions associated with a first trigger to activate a first set of resources, the first set of resources and instructions correspond to a first category, accessing a second set of resources associated with a second trigger to activate the second set of resources, the second set of resources and instructions correspond to a second category of user preferences, the second category different from the first category, modify the recipe to replace a first resource block of the first set of resources with a second resource block of the second set of resources, and deploy the recipe at a computing device, the computing device receiving at least one of the first trigger or the second trigger.

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store sensor data captured by one or more sensors associated with a first device. Further, the processor comprises circuitry to: access the sensor data captured by the one or more sensors associated with the first device; determine that an incident occurred within a vicinity of the first device; identify a first collection of sensor data associated with the incident, wherein the first collection of sensor data is identified from the sensor data captured by the one or more sensors; preserve, on the memory, the first collection of sensor data associated with the incident; and notify one or more second devices of the incident, wherein the one or more second devices are located within the vicinity of the first device.

Abstract: The present disclosure discloses a batch computing system and an associated method. The batch computing system includes a memory, a task manager and an inference computer. The memory stores a shared model parameter set common to a plurality of tasks that is generated by fine tuning a shared model and a task-specific parameter set of each task. The inference computer receives a plurality of task requests, derives a data length and a designated task of each task request, and enables the task manager to read a task-specific parameter set and a shared model parameter set corresponding to each task request. The inference computer further assigns task requests corresponding to the shared model to a plurality of computation batches, performs, in batch, the common computation of designated tasks in each batch computation, and performs task-specific computation operations of the designated tasks of each batch computation.

Abstract: The present application discloses a processor and a data operation method thereof. The processor includes: a first set of registers, a second set of registers and an execution unit. The execution unit is electrically connected to the first set of registers and the second set of registers, and configured to: convert data stored in the first set of registers from a first bit unit to a second bit unit, wherein the number of bits of the first bit unit is less than the number of bits of the second bit unit and the number of bits of the second bit unit is not an integer multiple of the number of bits of the first bit unit; and store the data in the second set of registers, wherein the number of registers of the second set of registers is greater than the number of registers of the first set of registers.

Abstract: This application describes methods, systems, and apparatus, for neural network-based program sampling (NPS). An example device may obtain an assembly code of a program and an execution trace of the program, and divide the assembly code into a plurality of execution intervals. The device may construct a plurality of code graphs respectively corresponding to the plurality of execution intervals, and for each of the plurality of code graphs: generate a plurality of graph snapshots based on the code graph and the execution trace of the program; embed, by using a Graph Neural Network, the plurality of graph snapshots into a plurality of vectors; and aggregate the plurality of vectors into an execution embedding. The device may cluster the plurality of execution embeddings into a plurality of clusters and select representative execution intervals of the program based on the plurality of clusters for execution.

Abstract: A number of domain specific accelerators (DSA1-DSAn) are integrated into a conventional processing system (100) to operate on the same chip by adding additional instructions to a conventional instruction set architecture (ISA), and further adding an accelerator interface unit (130) to the processing system (100) to respond to the additional instructions and interact with the DSAs.

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

Abstract: Transcoding bitrate prediction techniques can include receiving a first encoded content. A transcoder bitrate can be estimated based on regression over a video quality estimator of the first encoded content and a second encoded content. The estimated transcoder bitrate can be utilized to transcoding the first encoded content into the second encoded.

Abstract: The present disclosure relates to a method for compensating an image. The method comprises estimating transform coefficients of a frequency component for a first image based on the first image, performing a dot multiplication operation between the estimated transform coefficients and a basis function associated with the frequency component to generate a compensation image, and combining the first image and the compensation image to generate a combined image.

Abstract: A mechanism is described for facilitating portable, reusable, and shareable Internet of Things-based services and resources according to one embodiment. A method of embodiments, as described herein, includes wherein receiving a recipe request for selecting a recipe relating to Internet of Things (IoT) services, where selecting includes at least one of searching the recipe and modifying the recipe. The method may further include selecting the recipe, where the recipe includes a set of resource requirements and business logic. The method may further include modifying the set of resource requirements, where the modified set of resource requirements is associated with the business logic to modify the recipe, and deploying the modified recipe at one or more computing devices, where the modified recipe to facilitate management for one or more IoT devices at one or more locations.

Abstract: In one embodiment, an apparatus comprises a memory and a processor. The memory is to store visual data associated with a visual representation captured by one or more sensors. The processor is to: obtain the visual data associated with the visual representation captured by the one or more sensors, wherein the visual data comprises uncompressed visual data or compressed visual data; process the visual data using a convolutional neural network (CNN), wherein the CNN comprises a plurality of layers, wherein the plurality of layers comprises a plurality of filters, and wherein the plurality of filters comprises one or more pixel-domain filters to perform processing associated with uncompressed data and one or more compressed-domain filters to perform processing associated with compressed data; and classify the visual data based on an output of the CNN.

Abstract: Data is received describing a local model of a first device generated by the first device based on sensor readings at the first device and a global model is updated that is hosted remote from the first device based on the local model and modeling devices in a plurality of different asset taxonomies. A particular operating state affecting one or more of a set of devices deployed in a particular machine-to-machine network is detected and the particular machine-to-machine network is automatically reconfigured based on the global model.

Abstract: Scene aware video content encoding techniques can determine if video content is a given content type and is one of one or more given titles that include one or more given scenes. The one or more given scenes of the video content of the given type and given one of the titles can be encoded using corresponding scenes specific encoding parameter values, and the non-given scenes can be encoded using one or more general encoding parameter values. The one or more given titles can be selected based on a rate of streaming of various video content titles of the given type.

Abstract: In one embodiment, an apparatus comprises a communication interface and a processor. The communication interface is to communicate with a plurality of devices. The processor is to: receive compressed data from a first device, wherein the compressed data is associated with visual data captured by sensor(s); perform a current stage of processing on the compressed data using a current CNN, wherein the current stage of processing corresponds to one of a plurality of processing stages associated with the visual data, and wherein the current CNN corresponds to one of a plurality of CNNs associated with the plurality of processing stages; obtain an output associated with the current stage of processing; determine, based on the output, whether processing associated with the visual data is complete; if the processing is complete, output a result associated with the visual data; if the processing is incomplete, transmit the compressed data to a second device.

Abstract: User inputs received through a graphical user interface of a programming tool are used to define a set of capability abstractions for a particular application and further define, based on the one or more user inputs, relationships between the set of capability abstractions. The particular application is to utilize a machine-to-machine network, and the set of capability abstractions include: a sensor capability abstraction, an actuator capability abstraction, a computation logic capability abstraction, an input user interface (UI) capability abstraction, and an output UI capability abstraction for the particular application. The relationships include a relationship between the input UI capability abstraction and the computation logic capability abstraction, where the input UI capability is to provide an input to the computation logic capability abstraction.

Abstract: Data is received describing a local model of a first device generated by the first device based on sensor readings at the first device and a global model is updated that is hosted remote from the first device based on the local model and modeling devices in a plurality of different asset taxonomies. A particular operating state affecting one or more of a set of devices deployed in a particular machine-to-machine network is detected and the particular machine-to-machine network is automatically reconfigured based on the global model.

Abstract: The present disclosure relates to a computer-implemented method for processing video data. The method comprises receiving a user input corresponding to a first picture of the video data, generating, based on the user input, prediction information of the first picture with respect a reference picture of the video data, and encoding the first picture using the prediction information.