MEMORY CALIBRATION SYSTEM AND METHOD
    213.
    发明公开

    公开(公告)号:US20230368832A1

    公开(公告)日:2023-11-16

    申请号:US18198709

    申请日:2023-05-17

    Abstract: A method for performing stutter of dynamic random access memory (DRAM) where a system on a chip (SOC) initiates bursts of requests to the DRAM to fill buffers to allow the DRAM to self-refresh is disclosed. The method includes issuing, by a system management unit (SMU), a ForceZQCal command to the memory controller to initiate the stutter procedure in response to receiving a timeout request, such as an SMU ZQCal timeout request, periodically issuing a power platform threshold (PPT) request, by the SMU, to the memory controller, and sending a ForceZQCal command prior to a PPT request to ensure re-training occurs after ZQ Calibration. The ForceZQCal command issued prior to PPT request may reduce the latency of the stutter. The method may further include issuing a ForceZQCal command prior to each periodic re-training.

    Adaptive quantization for neural networks

    公开(公告)号:US11803734B2

    公开(公告)日:2023-10-31

    申请号:US15849617

    申请日:2017-12-20

    CPC classification number: G06N3/063 G06N3/08

    Abstract: Methods, devices, systems, and instructions for adaptive quantization in an artificial neural network (ANN) calculate a distribution of ANN information; select a quantization function from a set of quantization functions based on the distribution; apply the quantization function to the ANN information to generate quantized ANN information; load the quantized ANN information into the ANN; and generate an output based on the quantized ANN information. Some examples recalculate the distribution of ANN information and reselect the quantization function from the set of quantization functions based on the resampled distribution if the output does not sufficiently correlate with a known correct output. In some examples, the ANN information includes a set of training data. In some examples, the ANN information includes a plurality of link weights.

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