公开(公告)号：US10798379B2

公开(公告)日：2020-10-06

申请号：US16228350

申请日：2018-12-20

Applicant: TEXAS INSTRUMENTS INCORPORATED

Inventor： Soyeb Nagori ,  Manu Mathew ,  Pramod Kumar Swami

IPC: H04N19/107 ,  H04N19/50 ,  H04N19/176 ,  H04N19/147

Abstract: This invention predicts that intra mode prediction is more effective for the macroblocks where motion estimation in inter mode prediction fails. This failure is indicated by a large value of the inter mode SAD. This invention performs intra mode prediction for only macro blocks have larger inter mode SADs. The definition of a large inter mode SAD differs for different content. This invention compares the inter mode SAD of a current macroblock with an adaptive threshold. This adaptive threshold depends on the average and variance of the SADs of the previous predicted frame. An adaptive threshold is calculated for each new predictive frame.

公开(公告)号：US10325204B2

公开(公告)日：2019-06-18

申请号：US14792596

申请日：2015-07-06

Applicant: Texas Instruments Incorporated

Inventor： Shyam Jagannathan ,  Pramod Kumar Swami

IPC: G06F9/38 ,  G06N5/02 ,  G06K9/00 ,  G06K9/62

Abstract: A method for object classification in a decision tree based adaptive boosting (AdaBoost) classifier implemented on a single-instruction multiple-data (SIMD) processor is provided that includes receiving feature vectors extracted from N consecutive window positions in an image in a memory coupled to the SIMD processor and evaluating the N consecutive window positions concurrently by the AdaBoost classifier using the feature vectors and vector instructions of the SIMD processor, in which the AdaBoost classifier concurrently traverses decision trees for the N consecutive window positions until classification is complete for the N consecutive window positions.

公开(公告)号：US10165270B2

公开(公告)日：2018-12-25

申请号：US15419512

申请日：2017-01-30

Applicant: Texas Instruments Incorporated

Inventor： Soyeb Nagori ,  Manu Mathew ,  Pramod Kumar Swami

IPC: H04N19/107 ,  H04N19/176 ,  H04N19/147 ,  H04N19/50

Abstract: This invention predicts that intra mode prediction is more effective for the macroblocks where motion estimation in inter mode prediction fails. This failure is indicated by a large value of the inter mode SAD. This invention performs intra mode prediction for only macro blocks have larger inter mode SADs. The definition of a large inter mode SAD differs for different content. This invention compares the inter mode SAD of a current macroblock with an adaptive threshold. This adaptive threshold depends on the average and variance of the SADs of the previous predicted frame. An adaptive threshold is calculated for each new predictive frame.

公开(公告)号：US20180181864A1

公开(公告)日：2018-06-28

申请号：US15800322

申请日：2017-11-01

Applicant: Texas Instruments Incorporated

Inventor： Manu Mathew ,  Kumar Desappan ,  Pramod Kumar Swami

IPC: G06N3/08 ,  G06N3/04

CPC classification number: G06N3/04 ,  G06N3/0454 ,  G06N3/08 ,  G06N3/082 ,  G06N3/084 ,  G06N3/10

Abstract: A method for generating a sparsified convolutional neural network (CNN) is provided that includes training the CNN to generate coefficient values of filters of convolution layers, and performing sparsified fine tuning on the convolution layers to generate the sparsified CNN, wherein the sparsified fine tuning causes selected nonzero coefficient values of the filters to be set to zero.

公开(公告)号：US09508018B2

公开(公告)日：2016-11-29

申请号：US14551942

申请日：2014-11-24

Applicant: TEXAS INSTRUMENTS INCORPORATED

Inventor： Kumar Arrakutti Desappan ,  Manu Mathew ,  Pramod Kumar Swami

IPC: G06K9/00 ,  G06K9/46 ,  G06K9/62

CPC classification number: G06K9/4647 ,  G06K9/4652 ,  G06K9/6212

Abstract: An object detection system and a method of detecting an object in an image are disclosed. In an embodiment, a method for detecting the object includes computing one or more feature planes of one or more types for each image pixel of the image. A plurality of cells is defined in the image, where each cell includes first through nth number of pixels, and starting locations of each cell in the image in horizontal and vertical directions are integral multiples of predefined horizontal and vertical step sizes, respectively. One or more feature plane summations of one or more types are computed for each cell. A feature vector is determined for an image portion of the image based on a set of feature plane summations, and the feature vector is compared with a corresponding object classifier to detect a presence of the corresponding object in the image portion of the image.

Abstract translation: 公开了一种物体检测系统和检测图像中物体的方法。 在一个实施例中，用于检测对象的方法包括为图像的每个图像像素计算一个或多个类型的一个或多个特征面。 在图像中定义多个单元，其中每个单元包括第一至第n个像素数，并且图像中每个单元在水平和垂直方向上的起始位置分别是预定水平和垂直步长的整数倍。 为每个单元计算一个或多个类型的一个或多个特征平面求和。 基于特征平面求和的集合来确定图像的图像部分的特征向量，并且将特征向量与对应的对象分类器进行比较，以检测图像的图像部分中相应对象的存在。

公开(公告)号：US20240394543A1

公开(公告)日：2024-11-28

申请号：US18795565

申请日：2024-08-06

Applicant: Texas Instruments Incorporated

Inventor： Manu Mathew ,  Kumar Desappan ,  Soyeb Noormohammed Nagori ,  Debapriya Maji ,  Pramod Kumar Swami

IPC: G06N3/084 ,  G06N3/04 ,  G06N3/048

Abstract: In an example, a method includes executing, using one or more processors, a power-of-2 parametric activation (PACT2) function to quantize a set of data. The executing of the PACT2 function includes determining a distribution for the set of data; discarding a portion of the data corresponding to a tail of the distribution to form a remaining set of data; estimating a maximum value of the remaining set of data; determining a new maximum value of the remaining set of data using a moving average and at least one historical value of at least one prior remaining set of data; determining a clipping value by expanding the new maximum value to a nearest power of two value; and quantizing the set of data using the clipping value to form a quantized set of data.

公开(公告)号：US11995472B2

公开(公告)日：2024-05-28

申请号：US17378841

申请日：2021-07-19

Applicant: Texas Instruments Incorporated

Inventor： Mihir Narendra Mody ,  Kedar Satish Chitnis ,  Kumar Desappan ,  David Smith ,  Pramod Kumar Swami ,  Shyam Jagannathan

IPC: G06F9/50 ,  G06F9/455 ,  G06F12/00 ,  G06F12/02 ,  G06N3/02 ,  G06N3/10 ,  G06N20/00

CPC classification number: G06F9/5016 ,  G06F9/5077 ,  G06F12/00 ,  G06F12/0223 ,  G06F2009/45583 ,  G06F9/50 ,  G06F9/5022 ,  G06N3/02 ,  G06N3/10 ,  G06N20/00

Abstract: Techniques for executing machine learning (ML) models including receiving an indication to run an ML model on a processing core; receiving a static memory allocation for running the ML model on the processing core; determining that a layer of the ML model uses more memory than the static memory allocated; transmitting, to a shared memory, a memory request for blocks of the shared memory; receiving an allocation of the requested blocks; running the layer of the ML model using the static memory and the range of memory addresses; and outputting results of running the layer of the ML model.

公开(公告)号：US11856220B2

公开(公告)日：2023-12-26

申请号：US17844739

申请日：2022-06-21

Applicant: Texas Instruments Incorporated

Inventor： Soyeb Nagori ,  Arun Shankar Kudana ,  Pramod Kumar Swami

IPC: H04N19/52 ,  H04N19/523 ,  H04N19/105 ,  H04N19/176 ,  H04N19/147 ,  H04N19/172 ,  H04N19/61 ,  H04N19/109 ,  H04N19/114 ,  H04N19/117 ,  H04N19/156 ,  H04N19/157

CPC classification number: H04N19/523 ,  H04N19/105 ,  H04N19/109 ,  H04N19/114 ,  H04N19/117 ,  H04N19/147 ,  H04N19/156 ,  H04N19/157 ,  H04N19/172 ,  H04N19/176 ,  H04N19/61

Abstract: Several techniques aimed at reducing computational complexity when encoding uses bi-predictively encoded frames (B-frames) are implemented in a video encoder. In an embodiment, B-frames are not used as reference frames for encoding P-frames and other B-frames. Non-use of B-frames allows a de-blocking filter used in the video encoder to be switched off when reconstructing encoded B-frames, and use of a lower complexity filter for fractional-resolution motion search for B-frames. In another embodiment, cost functions used in motion estimation for B-frames are simplified to reduce computational complexity. In one more embodiment, fractional pixel refinement in motion search for B-frames is simplified. In yet another embodiment, predictors used in motion estimation for a macro-block in a P-frame are selected from a B-frame that uses a same reference frame as the P-frame.

公开(公告)号：US11847184B2

公开(公告)日：2023-12-19

申请号：US17149474

申请日：2021-01-14

Applicant: Texas Instruments Incorporated

Inventor： Deepak Kumar Poddar ,  Soyeb Nagori ,  Hrushikesh Tukaram Garud ,  Pramod Kumar Swami

IPC: G06K9/62 ,  G06F9/50 ,  G06F7/523 ,  G06F17/16 ,  G06F18/22 ,  G06V10/75

CPC classification number: G06F17/16 ,  G06F7/523 ,  G06F9/5027 ,  G06F18/22 ,  G06V10/75

Abstract: A matching accelerator in the form of a hardware accelerator configured to perform matrix multiplication and/or additional operations is used to optimize keypoint matching. An SSE calculation may be determined by utilizing the matching accelerator to perform matrix multiplication to obtain a cost matrix for two sets of keypoint descriptors from two images. The hardware accelerator may determine a best cost calculation for each keypoint in each direction, which is utilized to perform keypoint matching.

公开(公告)号：US11048997B2

公开(公告)日：2021-06-29

申请号：US15800294

申请日：2017-11-01

Applicant: Texas Instruments Incorporated

Inventor： Manu Mathew ,  Kumar Desappan ,  Pramod Kumar Swami

IPC: G06N3/04 ,  G06N3/08 ,  G06F7/544 ,  G06F17/15 ,  G06N3/10

Abstract: A method for convolution in a convolutional neural network (CNN) is provided that includes accessing a coefficient value of a filter corresponding to an input feature map of a convolution layer of the CNN, and performing a block multiply accumulation operation on a block of data elements of the input feature map, the block of data elements corresponding to the coefficient value, wherein, for each data element of the block of data elements, a value of the data element is multiplied by the coefficient value and a result of the multiply is added to a corresponding data element in a corresponding output block of data elements comprised in an output feature map.