Abstract: Methods, devices and systems for detecting suspicious or performance-degrading mobile device behaviors intelligently, dynamically, and/or adaptively determine computing device behaviors that are to be observed, the number of behaviors that are to be observed, and the level of detail or granularity at which the mobile device behaviors are to be observed. The various aspects efficiently identify suspicious or performance-degrading mobile device behaviors without requiring an excessive amount of processing, memory, or energy resources. Various aspects may correct suspicious or performance-degrading mobile device behaviors. Various aspects may prevent identified suspicious or performance-degrading mobile device behaviors from degrading the performance and power utilization levels of a mobile device over time. Various aspects may restore an aging mobile device to its original performance and power utilization levels.

Abstract: Systems and methods for recognizing and reacting to malicious or performance-degrading behaviors in a mobile computing device include observing mobile device behaviors in an observer module within a privileged-normal portion of a secure operating environment to identify a suspicious mobile device behavior. The observer module may generate a behavior vector based on the observations, and provide the vector to an analyzer module in an unprivileged-secure portion of the secure operating environment. The vector may be analyzed in the unprivileged-secure portion to determine whether the mobile device behavior is benign, suspicious, malicious, or performance-degrading. If the behavior is found to be suspicious, operations of the observer module may be adjusted, such as to perform deeper observations. If the behavior is found to be malicious or performance-degrading behavior the user and/or a client module may be alerted in a secure, tamper-proof manner.

Abstract: Methods, systems and devices for communicating behavior analysis information using an application programming interface (API) may include receiving data/behavior models from one or more third-party network servers in a client module of a mobile device and communicating the information to a behavior observation and analysis system via a behavior API. The third-party servers may be maintained by one or more partner companies that have domain expertise in a particular area or technology that is relevant for identifying, analyzing, classifying, and/or reacting to mobile device behaviors, but that do not have access to (or knowledge of) the various mobile device sub-systems, interfaces, configurations, modules, processes, drivers, and/or hardware systems required to generate effective data/behavior models suitable for use by the mobile device. The behavior API and/or client modules allow the third-party server to quickly and efficiently access the most relevant and important information on the mobile device.

Abstract: Systems and methods for recognizing and reacting to malicious or performance-degrading behaviors in a mobile computing device include observing mobile device behaviors in an observer module within a privileged-normal portion of a secure operating environment to identify a suspicious mobile device behavior. The observer module may generate a behavior vector based on the observations, and provide the vector to an analyzer module in an unprivileged-secure portion of the secure operating environment. The vector may be analyzed in the unprivileged-secure portion to determine whether the mobile device behavior is benign, suspicious, malicious, or performance-degrading. If the behavior is found to be suspicious, operations of the observer module may be adjusted, such as to perform deeper observations. If the behavior is found to be malicious or performance-degrading behavior the user and/or a client module may be alerted in a secure, tamper-proof manner.

Abstract: The various aspects provide a dynamic compilation framework that includes a machine-independent optimization module operating on a computing device and methods for optimizing code with the machine-independent optimization module using a single, combined-forwards-backwards pass of the code. In the various aspects, the machine-independent optimization module may generate a graph of nodes from the IR, optimize nodes in the graph using forwards and backwards optimizations, and propagating the forwards and backwards optimizations to nodes in a bounded subgraph recognized or defined based on the position of the node currently being optimized. In the various aspects, the machine-independent optimization module may optimize the graph by performing forwards and/or backwards optimizations during a single pass through the graph, thereby achieving an effective degree of optimization and shorter overall compile times.

Abstract: Methods, devices and systems for detecting suspicious or performance-degrading mobile device behaviors intelligently, dynamically, and/or adaptively determine computing device behaviors that are to be observed, the number of behaviors that are to be observed, and the level of detail or granularity at which the mobile device behaviors are to be observed. The various aspects efficiently identify suspicious or performance-degrading mobile device behaviors without requiring an excessive amount of processing, memory, or energy resources. Various aspects may correct suspicious or performance-degrading mobile device behaviors. Various aspects may prevent identified suspicious or performance-degrading mobile device behaviors from degrading the performance and power utilization levels of a mobile device over time. Various aspects may restore an aging mobile device to its original performance and power utilization levels.

Abstract: Methods, devices and systems for detecting suspicious or performance-degrading mobile device behaviors intelligently, dynamically, and/or adaptively determine computing device behaviors that are to be observed, the number of behaviors that are to be observed, and the level of detail or granularity at which the mobile device behaviors are to be observed. The various aspects efficiently identify suspicious or performance-degrading mobile device behaviors without requiring an excessive amount of processing, memory, or energy resources. Various aspects may correct suspicious or performance-degrading mobile device behaviors. Various aspects may prevent identified suspicious or performance-degrading mobile device behaviors from degrading the performance and power utilization levels of a mobile device over time. Various aspects may restore an aging mobile device to its original performance and power utilization levels.

Abstract: Methods, devices and systems for detecting suspicious or performance-degrading mobile device behaviors intelligently, dynamically, and/or adaptively determine computing device behaviors that are to be observed, the number of behaviors that are to be observed, and the level of detail or granularity at which the mobile device behaviors are to be observed. The various aspects efficiently identify suspicious or performance-degrading mobile device behaviors without requiring an excessive amount of processing, memory, or energy resources. Various aspects may correct suspicious or performance-degrading mobile device behaviors. Various aspects may prevent identified suspicious or performance-degrading mobile device behaviors from degrading the performance and power utilization levels of a mobile device over time. Various aspects may restore an aging mobile device to its original performance and power utilization levels.

Abstract: Methods, systems and devices for communicating behavior analysis information using an application programming interface (API) may include receiving data/behavior models from one or more third-party network servers in a client module of a mobile device and communicating the information to a behavior observation and analysis system via a behavior API. The third-party servers may be maintained by one or more partner companies that have domain expertise in a particular area or technology that is relevant for identifying, analyzing, classifying, and/or reacting to mobile device behaviors, but that do not have access to (or knowledge of) the various mobile device sub-systems, interfaces, configurations, modules, processes, drivers, and/or hardware systems required to generate effective data/behavior models suitable for use by the mobile device. The behavior API and/or client modules allow the third-party server to quickly and efficiently access the most relevant and important information on the mobile device.

Abstract: This invention describes a compilation method of extracting and implementing an accelerator control program from an application source code in a processor based system. The application source code comprises arrays and loops. The input application source code is sequential, with loop, branch and call control structures, while the generated output of this invention has parallel execution semantics. The compilation method comprises the step of performing loop nest analysis, transformations and backend processes. The step of loop nest analysis consists of dependence analysis and pointer analysis. Dependence analysis determines the conflicts between the various references to arrays in the loop, and pointer analysis determines if two pointer references in a loop are in conflict. Transformations convert the loops from their original sequential execution semantics to parallel execution semantics. The back-end process determines the parameters and memory map of the accelerator and the hardware dependent software.