Concurrent RGBZ sensor and system
    41.
    发明授权

    公开(公告)号:US11924545B2

    公开(公告)日:2024-03-05

    申请号:US17941000

    申请日:2022-09-08

    CPC classification number: H04N23/667 G06T7/521 H04N2213/003

    Abstract: Two-dimensional (2D) color information and 3D-depth information are concurrently obtained from a 2D pixel array. The 2D pixel array is arranged in a first group of a plurality of rows. A second group of rows of the array are operable to generate 2D-color information and pixels of a third group of the array are operable to generate 3D-depth information. The first group of rows comprises a first number of rows, the second group of rows comprises a second number of rows that is equal to or less than the first number of rows, and the third group of rows comprises a third number of rows that is equal to or less than the second number of rows. In an alternating manner, 2D-color information is received from a row selected from the second group of rows and 3D-depth information is received from a row selected from the third group of rows.

    Mixed-precision NPU tile with depth-wise convolution

    公开(公告)号:US11880760B2

    公开(公告)日:2024-01-23

    申请号:US16840172

    申请日:2020-04-03

    CPC classification number: G06N3/063 G06F9/3001 G06F9/30032 G06F13/1668

    Abstract: A processor to perform inference on deep learning neural network models. In some embodiments, the process includes: a first tile, a second tile, a memory, and a bus, the bus being connected to: the memory, the first tile, and the second tile, the first tile including: a first weight register, a second weight register, an activations cache, a shuffler, an activations buffer, a first multiplier, and a second multiplier, the activations buffer being configured to include: a first queue connected to the first multiplier, and a second queue connected to the second multiplier, the activations cache including a plurality of independent lanes, each of the independent lanes being randomly accessible, the first tile being configured: to receive a tensor including a plurality of two-dimensional arrays, each representing one color component of the image; and to perform a convolution of a kernel with one of the two-dimensional arrays.

    Neural processing accelerator
    46.
    发明授权

    公开(公告)号:US11360930B2

    公开(公告)日:2022-06-14

    申请号:US15916189

    申请日:2018-03-08

    Abstract: A system for calculating. A scratch memory is connected to a plurality of configurable processing elements by a communication fabric including a plurality of configurable nodes. The scratch memory sends out a plurality of streams of data words. Each data word is either a configuration word used to set the configuration of a node or of a processing element, or a data word carrying an operand or a result of a calculation. Each processing element performs operations according to its current configuration and returns the results to the communication fabric, which conveys them back to the scratch memory.

    Timestamp calibration of the 3D camera with epipolar line laser point scanning

    公开(公告)号:US10893227B2

    公开(公告)日:2021-01-12

    申请号:US16549577

    申请日:2019-08-23

    Abstract: Using the same image sensor to capture a two-dimensional (2D) image and three-dimensional (3D) depth measurements for a 3D object. A laser point-scans the surface of the object with light spots, which are detected by a pixel array in the image sensor to generate the 3D depth profile of the object using triangulation. Each row of pixels in the pixel array forms an epipolar line of the corresponding laser scan line. Timestamping provides a correspondence between the pixel location of a captured light spot and the respective scan angle of the laser to remove any ambiguity in triangulation. An Analog-to-Digital Converter (ADC) in the image sensor operates as a Time-to-Digital (TDC) converter to generate timestamps. A timestamp calibration circuit is provided on-board to record the propagation delay of each column of pixels in the pixel array and to provide necessary corrections to the timestamp values generated during 3D depth measurements.

    MIXED-PRECISION NPU TILE WITH DEPTH-WISE CONVOLUTION

    公开(公告)号:US20200349420A1

    公开(公告)日:2020-11-05

    申请号:US16840172

    申请日:2020-04-03

    Abstract: A processor to perform inference on deep learning neural network models. In some embodiments, the process includes: a first tile, a second tile, a memory, and a bus, the bus being connected to: the memory, the first tile, and the second tile, the first tile including: a first weight register, a second weight register, an activations cache, a shuffler, an activations buffer, a first multiplier, and a second multiplier, the activations buffer being configured to include: a first queue connected to the first multiplier, and a second queue connected to the second multiplier, the activations cache including a plurality of independent lanes, each of the independent lanes being randomly accessible, the first tile being configured: to receive a tensor including a plurality of two-dimensional arrays, each representing one color component of the image; and to perform a convolution of a kernel with one of the two-dimensional arrays.

    NEURAL PROCESSOR
    50.
    发明申请
    NEURAL PROCESSOR 审中-公开

    公开(公告)号:US20200026980A1

    公开(公告)日:2020-01-23

    申请号:US16552945

    申请日:2019-08-27

    Abstract: A neural processor. In some embodiments, the processor includes a first tile, a second tile, a memory, and a bus. The bus may be connected to the memory, the first tile, and the second tile. The first tile may include: a first weight register, a second weight register, an activations buffer, a first multiplier, and a second multiplier. The activations buffer may be configured to include: a first queue connected to the first multiplier and a second queue connected to the second multiplier. The first queue may include a first register and a second register adjacent to the first register, the first register being an output register of the first queue. The first tile may be configured: in a first state: to multiply, in the first multiplier, a first weight by an activation from the output register of the first queue, and in a second state: to multiply, in the first multiplier, the first weight by an activation from the second register of the first queue.

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