公开(公告)号：US11915431B2

公开(公告)日：2024-02-27

申请号：US16532658

申请日：2019-08-06

Applicant: TEXAS INSTRUMENTS INCORPORATED

Inventor： Deepak Kumar Poddar ,  Anshu Jain ,  Desappan Kumar ,  Pramod Kumar Swami

IPC: G06T7/246

CPC classification number: G06T7/246 ,  G06T2200/28 ,  G06T2207/20016 ,  G06T2207/30241

Abstract: A method for sparse optical flow based tracking in a computer vision system is provided that includes detecting feature points in a frame captured by a monocular camera in the computer vision system to generate a plurality of detected feature points, generating a binary image indicating locations of the detected feature points with a bit value of one, wherein all other locations in the binary image have a bit value of zero, generating another binary image indicating neighborhoods of currently tracked points, wherein locations of the neighborhoods in the binary image have a bit value of zero and all other locations in the binary image have a bit value of one, and performing a binary AND of the two binary images to generate another binary image, wherein locations in the binary image having a bit value of one indicate new feature points detected in the frame.

公开(公告)号：US20240036816A1

公开(公告)日：2024-02-01

申请号：US18193396

申请日：2023-03-30

Applicant: TEXAS INSTRUMENTS INCORPORATED

Inventor： Kumar Desappan ,  Anshu Jain ,  Manu Mathew

IPC: G06F5/01 ,  G06F7/485

CPC classification number: G06F5/012 ,  G06F7/485

Abstract: Disclosed herein are systems and methods for determining the scaling factors for a neural network that satisfy the activation functions employed by the nodes of the network. A processor identifies a saturation point of an activation function. Next, the processor determines a scaling factor for an output feature map based on the saturation point of the activation function. Then, the processor determines a scaling factor for an accumulator based on the scaling for the output feature map and further based on a shift value related to a quantization. Finally, the processor determines a scaling factor for a weight map based on the scaling factor for the accumulator.

公开(公告)号：US10460453B2

公开(公告)日：2019-10-29

申请号：US15266149

申请日：2016-09-15

Applicant: Texas Instruments Incorporated

Inventor： Deepak Kumar Poddar ,  Anshu Jain ,  Desappan Kumar ,  Pramod Kumar Swami

IPC: G06T7/246

Abstract: A method for sparse optical flow based tracking in a computer vision system is provided that includes detecting feature points in a frame captured by a monocular camera in the computer vision system to generate a plurality of detected feature points, generating a binary image indicating locations of the detected feature points with a bit value of one, wherein all other locations in the binary image have a bit value of zero, generating another binary image indicating neighborhoods of currently tracked points, wherein locations of the neighborhoods in the binary image have a bit value of zero and all other locations in the binary image have a bit value of one, and performing a binary AND of the two binary images to generate another binary image, wherein locations in the binary image having a bit value of one indicate new feature points detected in the frame.

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.

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

IPC: G06F17/15 ,  G06F7/544 ,  G06F17/16 ,  G06N3/04

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.

公开(公告)号：US20240046413A1

公开(公告)日：2024-02-08

申请号：US18175185

申请日：2023-02-27

Applicant: TEXAS INSTRUMENTS INCORPORATED

Inventor： Pramod Swami ,  Anshu Jain ,  Eppa Praveen Reddy ,  Kumar Desappan ,  Soyeb Nagori ,  Arthur Redfern

IPC: G06T3/40

CPC classification number: G06T3/4046

Abstract: Technology is disclosed herein to execute an inference model by a processor which includes a reshape layer. In an implementation, the reshape layer of the inference model receives an output produced by a previous layer of the inference model and inserts padding into the output, then supplies the padded output as an input to a next layer of the inference model. In an implementation, the inference model includes a stitching layer at the beginning of the inference model and an un-stitch layer at the end of the model. The stitching layer of the inference model stitches together multiple input images into an image batch and supplies the image batch as an input to a subsequent layer. The un-stitch layer receives output from a penultimate layer of the inference model and unstitches the output to produce multiple output images corresponding to the multiple input images.

公开(公告)号：US11748599B2

公开(公告)日：2023-09-05

申请号：US16797871

申请日：2020-02-21

Applicant: TEXAS INSTRUMENTS INCORPORATED

Inventor： Kumar Desappan ,  Mihir Narendra Mody ,  Pramod Kumar Swami ,  Anshu Jain ,  Rishabh Garg

IPC: G06F12/00 ,  G06N3/063 ,  G06T1/60 ,  G06F12/0804 ,  G06N3/08

CPC classification number: G06N3/063 ,  G06F12/0804 ,  G06N3/08 ,  G06T1/60

Abstract: Techniques including receiving a first set of values for processing by a machine learning (ML) network, storing a first portion of the first set of values in an on-chip memory, processing the first portion of the first set of values in a first layer of the ML network to generate a second portion of a second set of values, overwriting the stored first portion with the generated second portion, processing the second portion in a second layer of the ML network to generate a third portion of a third set of values, storing the third portion, repeating the steps of storing the first portion, processing the first portion, overwriting the stored first portion, processing the second portion, and storing the third portion for a fourth portion of the first set of values until all portions of the first set of values are processed to generate the third set of values.

公开(公告)号：US20170193669A1

公开(公告)日：2017-07-06

申请号：US15266149

申请日：2016-09-15

Applicant: Texas Instruments Incorporated

Inventor： Deepak Kumar Poddar ,  Anshu Jain ,  Desappan Kumar ,  Pramod Kumar Swami

IPC: G06T7/20 ,  G06K9/46 ,  G06T7/00

Abstract: A method for sparse optical flow based tracking in a computer vision system is provided that includes detecting feature points in a frame captured by a monocular camera in the computer vision system to generate a plurality of detected feature points, generating a binary image indicating locations of the detected feature points with a bit value of one, wherein all other locations in the binary image have a bit value of zero, generating another binary image indicating neighborhoods of currently tracked points, wherein locations of the neighborhoods in the binary image have a bit value of zero and all other locations in the binary image have a bit value of one, and performing a binary AND of the two binary images to generate another binary image, wherein locations in the binary image having a bit value of one indicate new feature points detected in the frame.