INTELLIGENT SENSORS FOR HIGH QUALITY SILICON LIFE CYCLE MANAGEMENT AND EFFICIENT INFIELD TESTING

    公开(公告)号:US20240220312A1

    公开(公告)日:2024-07-04

    申请号:US18091810

    申请日:2022-12-30

    CPC classification number: G06F9/4812 G06F11/263

    Abstract: Methods and apparatus relating to intelligent sensors for high quality silicon life cycle management as well as efficient infield structural and/or functional testing are described. In an embodiment, one or more registers store configuration data. A sensor having sensor event detection logic circuitry detects an event based at least in part on one or more sensor signals and the stored configuration data. The sensor event detection logic circuitry generates a signal to cause interrupt generator logic circuitry of the sensor to generate an interrupt. Other embodiments are also disclosed and claimed.

    Transform and inverse transform circuit and method

    公开(公告)号:US09918088B2

    公开(公告)日:2018-03-13

    申请号:US14458524

    申请日:2014-08-13

    Abstract: A transform and inverse transform circuit is provided. The transform and inverse transform circuit includes: at least one quantization and inverse quantization circuit, including at least one quantization and inverse quantization unit, wherein each quantization and inverse quantization unit includes a plurality of first coefficients, and each quantization and inverse quantization unit performs quantization or inverse quantization on one of multiple ways of inputting data; and at least one one-dimensional transform circuit, coupled to the quantization and inverse quantization circuit, wherein the one-dimensional transform circuit includes a plurality of second coefficients, wherein the one-dimensional transform circuit performs one-dimensional transform on the inputting data processed by the quantization and inverse quantization circuit, wherein the plurality of first coefficients and the plurality of second coefficients are set up based on a video codec standard.

    DEVICE, METHOD AND SYSTEM FOR IN-FIELD LANE TESTING AND REPAIR WITH A THREE-DIMENSIONAL INTEGRATED CIRCUIT

    公开(公告)号:US20250052809A1

    公开(公告)日:2025-02-13

    申请号:US18233199

    申请日:2023-08-11

    Abstract: Techniques and mechanisms for an integrated circuit (IC) die to support in-field testing and/or repair of a lane in a three-dimensional (3D) IC which is formed with multiple IC dies. In an embodiment, the 3D IC comprises test units which each correspond to a different partition comprising respective circuit resources. During in-field operation of the 3D IC, a given test unit is operable to detect both a first condition wherein two partitions are each in an idle state, and a second condition wherein a first link between the two partitions fails to satisfy a performance criteria. The idle states are indicated by a power management controller (PMC) agent during runtime operation of the 3D IC. In another embodiment, one or more test units configure an operational mode, based on the detected conditions, to substitute communication via the first lane with communication via a repair lane.

    MEMORY AND COMPUTE-EFFICIENT UNSUPERVISED ANOMALY DETECTION FOR INTELLIGENT EDGE PROCESSING

    公开(公告)号:US20220365523A1

    公开(公告)日:2022-11-17

    申请号:US17747479

    申请日:2022-05-18

    Abstract: Systems, apparatuses, and methods include technology that identifies a first dataset that comprises a plurality of data values, and partitions the first dataset into a plurality of bins to generate a second dataset, where the second dataset is a compressed version of the first dataset. The technology randomly subsamples data associated with the first dataset to obtain groups of randomly subsampled data, and generates a plurality of decision tree models during an unsupervised learning process based on the groups of randomly subsampled data and the second dataset.

    Analog functional safety with anomaly detection

    公开(公告)号:US10685159B2

    公开(公告)日:2020-06-16

    申请号:US16020396

    申请日:2018-06-27

    Abstract: In some examples, systems and methods may be used to improve functional safety of analog or mixed-signal circuits, and, more specifically, to anomaly detection to help predict failures for mitigating catastrophic results of circuit failures. An example may include using a machine learning model trained to identify point anomalies, contextual or conditional anomalies, or collective anomalies in a set of time-series data collected from in-field detectors of the circuit. The machine learning models may be trained with data that has only normal data or has some anomalous data included in the data set. In an example, the data may include functional or design-for-feature (DFx) signal data received from an in-field detector on an analog component. A functional safety action may be triggered based on analysis of the functional or DFx signal data.

    ANALOG FUNCTIONAL SAFETY WITH ANOMALY DETECTION

    公开(公告)号:US20190050515A1

    公开(公告)日:2019-02-14

    申请号:US16020396

    申请日:2018-06-27

    Abstract: In some examples, systems and methods may be used to improve functional safety of analog or mixed-signal circuits, and, more specifically, to anomaly detection to help predict failures for mitigating catastrophic results of circuit failures. An example may include using a machine learning model trained to identify point anomalies, contextual or conditional anomalies, or collective anomalies in a set of time-series data collected from in-field detectors of the circuit. The machine learning models may be trained with data that has only normal data or has some anomalous data included in the data set. In an example, the data may include functional or design-for-feature (DFx) signal data received from an in-field detector on an analog component. A functional safety action may be triggered based on analysis of the functional or DFx signal data.

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