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公开(公告)号：US20240062059A1
公开(公告)日：2024-02-22
申请号：US18191700
申请日：2023-03-28
Applicant: TEXAS INSTRUMENTS INCORPORATED
Inventor： Manu Mathew , Anand Pathak , Anshu Jain , Kumar Desappan
IPC: G06N3/08
CPC classification number: G06N3/08
Abstract: Various examples disclosed herein relate to neural network quantization techniques, and more particularly, to selecting inference precisions for the layers of the neural network. In an example embodiment, a method is provided herein that includes determining an accuracy improvement of a layer of a neural network implemented using a first bit precision relative to using a second bit precision and determining a latency degradation of the layer of the neural network implemented using the first bit precision relative to using the second bit precision. The method further includes selecting, based on the accuracy improvement and the latency degradation, the first bit precision or the second bit precision for use in implementing the layer of the neural network.
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公开(公告)号：US20220164411A1
公开(公告)日：2022-05-26
申请号：US17528472
申请日：2021-11-17
Applicant: Texas Instruments Incorporated
Inventor： Anshu Jain , Manu Mathew , Kumar Desappan , Anand Anil Pathak
Abstract: In described examples, an integrated circuit includes a memory storing weights and biases, an N-bit fixed point matrix operations accelerator, and a processor. Starting with a first convolution layer, a convolution layer modeled using the processor receives input feature values. A feature scale and weight scale are reduced if an accumulator scale is greater than a maximum bias scale. The input feature values are rescaled using the feature scale, the weights are quantized using the weight scale, and the biases are quantized using the feature scale and weight scale. The rescaled input feature values and quantized weights and biases are convolved using the N-bit fixed point matrix operations accelerator to generate output feature values. The process repeats from the receive action using the output feature values as the input feature values of the next convolution layer. The process then repeats for all layers, feeding back an output feature range.
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公开(公告)号：US20210037252A1
公开(公告)日：2021-02-04
申请号：US17075053
申请日：2020-10-20
Applicant: Texas Instruments Incorporated
Inventor： Soyeb Nagori , Arun Shankar Kudana , Manu Mathew
IPC: H04N19/196 , H04N19/142 , H04N19/177 , H04N19/152 , H04N19/126
Abstract: A method of rate control in coding of a video sequence to generate a compressed bit stream is provided that includes computing a sequence base quantization step size for a sequence of pictures in the video sequence, computing a picture base quantization step size for a picture in the sequence of pictures based on the sequence base quantization step size, a type of the picture, and a level of the picture in a rate control hierarchy, and coding the picture using the picture base quantization step size to generate a portion of the compressed bit stream.
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公开(公告)号：US10657389B2
公开(公告)日：2020-05-19
申请号：US16434542
申请日：2019-06-07
Applicant: TEXAS INSTRUMENTS INCORPORATED
Inventor： Prashanth Ramanathpur Viswanath , Soyeb Nagori , Manu Mathew
Abstract: A vehicular structure from motion (SfM) system can store a number of image frames acquired from a vehicle-mounted camera in a frame stack according to a frame stack update logic. The SfM system can detect feature points, generate flow tracks, and compute depth values based on the image frames, the depth values to aid control of the vehicle. The frame stack update logic can select a frame to discard from the stack when a new frame is added to the stack, and can be changed from a first in, first out (FIFO) logic to last in, first out (LIFO) logic upon a determination that the vehicle is stationary. An optical flow tracks logic can also be modified based on the determination. The determination can be made based on a dual threshold comparison to insure robust SfM system performance.
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公开(公告)号：US20200026933A1
公开(公告)日：2020-01-23
申请号：US16272415
申请日：2019-02-11
Applicant: Texas Instruments Incorporated
Inventor： Prashanth Ramanathpur Viswanath , Deepak Kumar Poddar , Soyeb Nagori , Manu Mathew
IPC: G06K9/00
Abstract: A method for estimating time to collision (TTC) of a detected object in a computer vision system is provided that includes determining a three dimensional (3D) position of a camera in the computer vision system, determining a 3D position of the detected object based on a 2D position of the detected object in an image captured by the camera and an estimated ground plane corresponding to the image, computing a relative 3D position of the camera, a velocity of the relative 3D position, and an acceleration of the relative 3D position based on the 3D position of the camera and the 3D position of the detected object, wherein the relative 3D position of the camera is relative to the 3D position of the detected object, and computing the TTC of the detected object based on the relative 3D position, the velocity, and the acceleration.
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Patent Agency Ranking
公开(公告)号：US20190244036A1
公开(公告)日：2019-08-08
申请号：US16268200
申请日：2019-02-05
Applicant: TEXAS INSTRUMENTS INCORPORATED
Inventor： Hrushikesh Tukaram Garud , Manu Mathew , Soyeb Noormohammed Nagori
CPC classification number: G06K9/00791 , G06K9/00758 , G06K9/52 , G06T7/207 , G06T7/223 , G06T7/238 , G06T7/269 , G06T2207/10016 , G06T2207/20016 , G06T2207/20032 , H04N19/53
Abstract: An image processing system includes a processor and optical flow (OF) determination logic for quantifying relative motion of a feature present in a first frame of video and a second frame of video that provide at least one of temporally and spatially ordered images with respect to the two frames of video. The OF determination logic configures the processor to implement performing OF estimation between the first frame and second frame using a pyramidal block matching (PBM) method to generate an initial optical flow (OF) estimate at a base pyramid level having integer pixel resolution, and refining the initial OF estimate using at least one pass of a modified Lucas-Kanade (LK) method to provide a revised OF estimate having fractional pixel resolution.
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公开(公告)号：US10354149B2
公开(公告)日：2019-07-16
申请号：US16121012
申请日：2018-09-04
Applicant: TEXAS INSTRUMENTS INCORPORATED
Inventor： Prashanth Ramanathpu Viswanath , Soyeb Nagori , Manu Mathew
Abstract: A vehicular structure from motion (SfM) system can store a number of image frames acquired from a vehicle-mounted camera in a frame stack according to a frame stack update logic. The SfM system can detect feature points, generate flow tracks, and compute depth values based on the image frames, the depth values to aid control of the vehicle. The frame stack update logic can select a frame to discard from the stack when a new frame is added to the stack, and can be changed from a first in, first out (FIFO) logic to last in, first out (LIFO) logic upon a determination that the vehicle is stationary. An optical flow tracks logic can also be modified based on the determination. The determination can be made based on a dual threshold comparison to insure robust SfM system performance.
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公开(公告)号：US10268901B2
公开(公告)日：2019-04-23
申请号：US15081118
申请日：2016-03-25
Applicant: Texas Instruments Incorporated
Inventor： Hrushikesh Tukaram Garud , Manu Mathew , Soyeb Noormohammed Nagori
Abstract: An image processing system includes a processor and optical flow (OF) determination logic for quantifying relative motion of a feature present in a first frame of video and a second frame of video that provide at least one of temporally and spatially ordered images with respect to the two frames of video. The OF determination logic configures the processor to implement performing OF estimation between the first frame and second frame using a pyramidal block matching (PBM) method to generate an initial optical flow (OF) estimate at a base pyramid level having integer pixel resolution, and refining the initial OF estimate using at least one pass of a modified Lucas-Kanade (LK) method to provide a revised OF estimate having fractional pixel resolution.
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公开(公告)号：US10255511B2
公开(公告)日：2019-04-09
申请号：US15395141
申请日：2016-12-30
Applicant: TEXAS INSTRUMENTS INCORPORATED
Inventor： Arun Shankar Kudana , Manu Mathew , Soyeb Nagori
Abstract: Advanced driver assistance systems need to be able to recognize and to classify traffic signs under real time constraints, and under a wide variety of visual conditions. The invention shown employs binary masks extracted by color space segmentation, with a different binary mask generated for each sign shape. Temporal tracking is employed to add robustness to the detection system. The system is generic, and is trainable to the signs used in various countries.
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公开(公告)号：US20190019043A1
公开(公告)日：2019-01-17
申请号：US16121012
申请日：2018-09-04
Applicant: TEXAS INSTRUMENTS INCORPORATED
Inventor： Prashanth Ramanathpu Viswanath , Soyeb Nagori , Manu Mathew
CPC classification number: G06K9/00791 , G06K9/4604 , G06T7/269 , G06T7/579 , G06T2207/30244 , G06T2207/30252
Abstract: A vehicular structure from motion (SfM) system can store a number of image frames acquired from a vehicle-mounted camera in a frame stack according to a frame stack update logic. The SfM system can detect feature points, generate flow tracks, and compute depth values based on the image frames, the depth values to aid control of the vehicle. The frame stack update logic can select a frame to discard from the stack when a new frame is added to the stack, and can be changed from a first in, first out (FIFO) logic to last in, first out (LIFO) logic upon a determination that the vehicle is stationary. An optical flow tracks logic can also be modified based on the determination. The determination can be made based on a dual threshold comparison to insure robust SfM system performance.
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