Methods and systems for selecting quantisation parameters for deep neural networks using back-propagation

    公开(公告)号:US11610127B2

    公开(公告)日:2023-03-21

    申请号:US16724650

    申请日:2019-12-23

    摘要: Methods and systems for identifying quantisation parameters for a Deep Neural Network (DNN). The method includes determining an output of a model of the DNN in response to training data, the model of the DNN comprising one or more quantisation blocks configured to transform a set of values input to a layer of the DNN prior to processing the set of values in accordance with the layer, the transformation of the set of values simulating quantisation of the set of values to a fixed point number format defined by one or more quantisation parameters; determining a cost metric of the DNN based on the determined output and a size of the DNN based on the quantisation parameters; back-propagating a derivative of the cost metric to one or more of the quantisation parameters to generate a gradient of the cost metric for each of the one or more quantisation parameters; and adjusting one or more of the quantisation parameters based on the gradients.

    TEXTURE ADDRESS GENERATION USING FRAGMENT PAIR DIFFERENCES

    公开(公告)号:US20230083265A1

    公开(公告)日:2023-03-16

    申请号:US17882999

    申请日:2022-08-08

    发明人: Rostam King

    IPC分类号: G06T15/04

    摘要: Methods and hardware for texture address generation comprise receiving fragment coordinates for an input block of fragments and texture instructions for the fragments and calculating gradients for at least one pair of fragments. Based on the gradients, the method determines whether a first mode or a second mode of texture address generation is to be used and then uses the determined mode and the gradients to perform texture address generation. The first mode of texture address generation performs calculations at a first precision for a subset of the fragments and calculations for remaining fragments at a second, lower, precision. The second mode of texture address generation performs calculations for all fragments at the first precision and if the second mode is used and more than half of the fragments in the input block are valid, the texture address generation is performed over two clock cycles.

    Method for center twisting wires
    47.
    发明授权

    公开(公告)号:US11600409B2

    公开(公告)日:2023-03-07

    申请号:US17689353

    申请日:2022-03-08

    摘要: A method of twisting a pair of wires includes the steps of arranging a first wire parallel to a second wire along a longitudinal axis, securing ends of the first and second wires, and gripping outer surfaces of central portions of the first and second wires. Inner surfaces of the central portions of the first and second wires are in contact with one another. The method further includes the step of rotating the central portions of the first and second wires, thereby twisting the first and second wires about one another.

    Vehicle control system
    48.
    发明授权

    公开(公告)号:US11597408B2

    公开(公告)日:2023-03-07

    申请号:US16924638

    申请日:2020-07-09

    摘要: A vehicle control system includes a controller circuit in communication with a steering sensor and one or more perception sensors. The steering sensor is configured to detect a steering torque of a steering wheel of a host vehicle. The one or more perception sensors are configured to detect an environment proximate the host vehicle. The controller circuit is configured to determine when an operator of the host vehicle requests a take-over from fully automated control of the host vehicle based on the steering sensor. The controller circuit classifies the take-over request based on the steering sensor.

    NUMBER FORMAT SELECTION FOR BIDIRECTIONAL RECURRENT NEURAL NETWORKS

    公开(公告)号:US20230068394A1

    公开(公告)日:2023-03-02

    申请号:US17852964

    申请日:2022-06-29

    IPC分类号: G06N3/063 G06N3/04

    摘要: A computer-implemented method of selecting a number format for use in configuring a hardware implementation of a bidirectional recurrent neural network (BRNN) for operation on a sequence of inputs. A received BRNN representation is implemented as a test neural network equivalent to the BRNN over a sequence of input tensors, each step of the test neural network being for operation on (a) an input tensor of the sequence, (b) a corresponding backward state tensor generated in respect of a subsequent input tensor of the sequence, and (c) a corresponding forward state tensor generated in respect of a preceding input tensor of the sequence. The test neural network includes a forward recurrent neural network (RNN) for operation on the forward state tensors over the input tensors of the sequence; and a backward RNN for operation on the backward state tensors over the input tensors of the sequence. A number format selection algorithm is applied to collected operating statistics so as to derive a common number format for a plurality of instances of one or more selected tensors of the test neural network.