Random number generator
    14.
    发明授权

    公开(公告)号:US12019510B2

    公开(公告)日:2024-06-25

    申请号:US17684198

    申请日:2022-03-01

    CPC classification number: G06F11/10 G06F7/58 G06F11/26

    Abstract: The present disclosure relates to a circuit for testing a random number generator adapted to delivering a series of random bits and comprising at least one test unit configured to detect a defect in the series of random bits, said test circuit being adapted to verifying whether, after the detection of a first defect by the test unit, the number of random bits, generated by the random number generator without the detection of a second defect by said unit test, is smaller than a first threshold.

    Voltage converter and method
    17.
    发明授权

    公开(公告)号:US11967900B2

    公开(公告)日:2024-04-23

    申请号:US17366353

    申请日:2021-07-02

    CPC classification number: H02M3/158 H02M1/0032 H02M1/0083 H02M1/36

    Abstract: An embodiment voltage converter includes a first transistor connected between a first node of the converter and a second node configured to receive a power supply voltage, a second transistor connected between the first node and a third node configured to receive a reference potential, a first circuit configured to control the first and second transistors, and a comparator including first and second inputs. The first input is configured to receive, during a first phase, a first voltage ramp and, during a second phase, a set point voltage. The second input is configured to receive, during the first phase, the set point voltage and, during the second phase, a second voltage ramp.

    ARTIFICIAL NEURON NETWORK HAVING AT LEAST ONE UNIT CELL QUANTIFIED IN BINARY

    公开(公告)号:US20240095502A1

    公开(公告)日:2024-03-21

    申请号:US18470281

    申请日:2023-09-19

    CPC classification number: G06N3/0464

    Abstract: An artificial neural network includes a unit cell. The unit cell includes a first binary two-dimensional convolution layer configured to receive an input tensor and to generate a first tensor. A first batch normalization layer is configured to receive the first tensor and to generate a second tensor. A concatenation layer is configured to generate a third tensor by concatenating the input tensor and the second tensor. A second binary two-dimensional convolution layer is configured to receive the third tensor and to generate a fourth tensor. A second batch normalization layer is configured to generate an output tensor based on the fourth tensor.

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