Abstract: A computing system that enables data stored in a persistent memory region to be preserved when a processor fails can include volatile memory comprising the persistent memory region, non-volatile memory, and a system on a chip (SoC). The SoC can include a main processor that is communicatively coupled to both the volatile memory and the non-volatile memory. The SoC can also include an auxiliary processor that is communicatively coupled to both the volatile memory and the non-volatile memory. The SoC can also include instructions that are executable by the auxiliary processor to cause the data in the persistent memory region of the volatile memory to be transferred to the non-volatile memory in response to a failure of the main processor.

Abstract: A deployment manager executing in a distributed computing environment generates a user behavior analytics (UBA) deployment to process structured event data. The deployment manager configures a streaming cluster to perform streaming processing on real-time data and configures a batch cluster to perform batch processing on aggregated data. A configuration manager executing in the distributed computing environment interoperates with the deployment manager to update the UBA deployment with user-provided code and configurations that define streaming and batch models, among other things. In this manner, the deployment manager provides a scalable UBA deployment that can be customized, via the configuration manager, by a user.

Abstract: A deployment manager executing in a distributed computing environment generates a user behavior analytics (UBA) deployment to process structured event data. The deployment manager configures a streaming cluster to perform streaming processing on real-time data and configures a batch cluster to perform batch processing on aggregated data. A configuration manager executing in the distributed computing environment interoperates with the deployment manager to update the UBA deployment with user-provided code and configurations that define streaming and batch models, among other things. In this manner, the deployment manager provides a scalable UBA deployment that can be customized, via the configuration manager, by a user.

Abstract: A deployment manager executing in a distributed computing environment generates a user behavior analytics (UBA) deployment to process structured event data. The deployment manager configures a streaming cluster to perform streaming processing on real-time data and configures a batch cluster to perform batch processing on aggregated data. A configuration manager executing in the distributed computing environment interoperates with the deployment manager to update the UBA deployment with user-provided code and configurations that define streaming and batch models, among other things. In this manner, the deployment manager provides a scalable UBA deployment that can be customized, via the configuration manager, by a user.

Abstract: A deployment manager executing in a distributed computing environment generates a user behavior analytics (UBA) deployment to process structured event data. The deployment manager configures a streaming cluster to perform streaming processing on real-time data and configures a batch cluster to perform batch processing on aggregated data. A configuration manager executing in the distributed computing environment interoperates with the deployment manager to update the UBA deployment with user-provided code and configurations that define streaming and batch models, among other things. In this manner, the deployment manager provides a scalable UBA deployment that can be customized, via the configuration manager, by a user.

Abstract: A deployment manager executing in a distributed computing environment generates a user behavior analytics (UBA) deployment to process structured event data. The deployment manager configures a streaming cluster to perform streaming processing on real-time data and configures a batch cluster to perform batch processing on aggregated data. A configuration manager executing in the distributed computing environment interoperates with the deployment manager to update the UBA deployment with user-provided code and configurations that define streaming and batch models, among other things. In this manner, the deployment manager provides a scalable UBA deployment that can be customized, via the configuration manager, by a user.

Abstract: A deployment manager executing in a distributed computing environment generates a user behavior analytics (UBA) deployment to process structured event data. The deployment manager configures a streaming cluster to perform streaming processing on real-time data and configures a batch cluster to perform batch processing on aggregated data. A configuration manager executing in the distributed computing environment interoperates with the deployment manager to update the UBA deployment with user-provided code and configurations that define streaming and batch models, among other things. In this manner, the deployment manager provides a scalable UBA deployment that can be customized, via the configuration manager, by a user.

Abstract: A technique for decoding data within a context-based adaptive binary arithmetic coding (CABAC) stream processes one or more bins of compressed data based on video format parameters associated with the stream. A configurable CABAC decoder circuit cascades one or more instances of CABAC bin decoder logic to operate properly within a timing constrain established by a decoder clock frequency. The decoder may advantageously select among different combinations of decoder clock frequency and decoded bins per clock cycle to minimize power consumption associated with decompressing and playing the compressed data.

Abstract: The description is directed to systems and methods for a low-power, hands-free voice triggering of a main processing complex of a computing system to wake from a suspended state. An always-on voice activity detection module samples output received from a microphone in the computing system and determines whether a portion of the sampled output potentially contains a triggering keyphrase. A special purpose audio processing engine is turned on to confirm the presence of the triggering keyphrase in the sampled output before triggering the main processing complex of the computing system to wake from the suspended state.

Abstract: A framework for efficient sum of absolute difference (SAD) computations for variable block size, sub-pixel motion estimation is presented. Simultaneous, or parallelized, SAD computations can be performed by storing and re-using previous SAD computational information, which can speed up the performance of a motion estimation module by reducing the number of cycles necessary to perform a particular motion estimation algorithm.

Abstract: A technique for decoding data within a context-based adaptive binary arithmetic coding (CABAC) stream processes one or more bins of compressed data based on video format parameters associated with the stream. A configurable CABAC decoder circuit cascades one or more instances of CABAC bin decoder logic to operate properly within a timing constrain established by a decoder clock frequency. The decoder may advantageously select among different combinations of decoder clock frequency and decoded bins per clock cycle to minimize power consumption associated with decompressing and playing the compressed data.

Abstract: A method and system for performing a 2D transform is disclosed. The 2D transform may include a row transform and/or a column transform. When performing the row or column transform, it may be determined whether each of different subsets of the data values including a partition of a row or column includes at least one zero value, whether each of different subsets of a first subset of the partition includes at least one zero value, and whether each of different subsets of at least one other subset of the partition includes at least one zero value. When performing the row or column transform, at least one transformation operation on at least one zero value may be bypassed or performed in a reduced-power manner, where such transformation operation would otherwise be performed in a manner consuming full power if the zero value were a non-zero value.

Abstract: In some embodiments, a method for performing and a system configured to perform a 2D transform (for example, an inverse discrete cosine transform) on each block of a sequence of data blocks, where the 2D transform includes a row transform and a column transform. To perform the row or column transform on a row or column of data, these embodiments determine whether each of different subsets of the data values comprising a partition of the row (column) includes at least one zero value, whether each of different subsets of a first subset of the partition includes at least one zero value, and whether each of different subsets of at least one other subset of the partition includes at least one zero value.

Abstract: A clock module is coupled in parallel to a number of data processing modules that are coupled in series. The data processing modules can be individually clock-gated. Each of the data processing modules can determine whether or not it can be placed into an idle state. To reduce power consumption, any subset of the data processing modules that are eligible to be placed in an idle state can be clock-gated. The remaining data processing modules can continue to receive clock signals from the clock module and thus can continue to process data.

Abstract: A method and system for performing a 2D transform is disclosed. The 2D transform may include a row transform and/or a column transform. When performing the row or column transform, it may be determined whether each of different subsets of the data values including a partition of a row or column includes at least one zero value, whether each of different subsets of a first subset of the partition includes at least one zero value, and whether each of different subsets of at least one other subset of the partition includes at least one zero value. When performing the row or column transform, at least one transformation operation on at least one zero value may be bypassed or performed in a reduced-power manner, where such transformation operation would otherwise be performed in a manner consuming full power if the zero value were a non-zero value.

Abstract: A clock module is coupled in parallel to a number of data processing modules that are coupled in series. The data processing modules can be individually clock-gated. Each of the data processing modules can determine whether or not it can be placed into an idle state. To reduce power consumption, any subset of the data processing modules that are eligible to be placed in an idle state can be clock-gated. The remaining data processing modules can continue to receive clock signals from the clock module and thus can continue to process data.

Abstract: In some embodiments, a method for performing and a system configured to perform a 2D transform (for example, an inverse discrete cosine transform) on each block of a sequence of data blocks, where the 2D transform includes a row transform and a column transform. To perform the row or column transform on a row or column of data, these embodiments determine whether each of different subsets of the data values comprising a partition of the row (column) includes at least one zero value, whether each of different subsets of a first subset of the partition includes at least one zero value, and whether each of different subsets of at least one other subset of the partition includes at least one zero value.