ANOMALOUS BEHAVIOR DETECTION BY AN ARTIFICIAL INTELLIGENCE-ENABLED SYSTEM WITH MULTIPLE CORRELATED SENSORS

    公开(公告)号:US20220299985A1

    公开(公告)日:2022-09-22

    申请号:US17207540

    申请日:2021-03-19

    Abstract: Multi-metric artificial intelligence (AI)/machine learning (ML) models for detection of anomalous behavior of a machine/system are disclosed. The multi-metric AI/ML models are configured to detect anomalous behavior of systems having multiple sensors that measure correlated sensor metrics such as coolant distribution units (CDUs). The multi-metric AI/ML models perform the anomalous system behavior detection in a manner that enables both a reduction in the amount of sensor instrumentation needed to monitor the system's operational behavior as well as a corresponding reduction in the complexity of the firmware that controls the sensor instrumentation. As such, AI-enabled systems and corresponding methods for anomalous behavior detection disclosed herein offer a technical solution to the technical problem of increased failure rates of existing multi-sensor systems, which is caused by the presence of redundant sensor instrumentation that necessitates complex firmware for controlling the sensor instrumentation.

    CAMS FOR LOW LATENCY COMPLEX DISTRIBUTION SAMPLING

    公开(公告)号:US20230197151A1

    公开(公告)日:2023-06-22

    申请号:US17555260

    申请日:2021-12-17

    Abstract: Systems and methods are provided for employing analog content addressable memory (aCAMs) to achieve low latency complex distribution sampling. For example, an aCAM core circuit can include an aCAM array. Amplitudes of a probability distribution function are mapped to a width of one or more aCAM cells in each row of the aCAM array. The aCAM core circuit can also include a resistive random access memory (RRAM) storing lookup information, such as information used for processing a model. By randomly selecting columns to search of the aCAM array, the mapped probability distribution function is sampled in a manner that has low latency. The aCAM core circuit can accelerate the sampling step in methods relying on sampling from arbitrary probability distributions, such as particle filter techniques. A hardware architecture for an aCAM Particle Filter that utilizes the aCAM core circuit as a central structure is also described.

    IMPROVING DATA MONITORING AND QUALITY USING AI AND MACHINE LEARNING

    公开(公告)号:US20220404235A1

    公开(公告)日:2022-12-22

    申请号:US17351085

    申请日:2021-06-17

    Abstract: Systems and methods are provided for improving statistical and machine learning drift detection models that monitor computing health of a data center environment. For example, the system can receive streams of sensor data from a plurality of sensors in a data center; clean the streams of sensor data; generate, using a machine learning (ML) model, an anomaly score and a dynamic threshold value based on the cleaned streams of sensor data; determine, using the ML model and based on the anomaly score and the dynamic threshold value, a correctness indicator for a first sensor in the plurality of sensors; and using the correctness indicator, correct the first sensor.

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