公开(公告)号：US20220326909A1

公开(公告)日：2022-10-13

申请号：US17714327

申请日：2022-04-06

Applicant: TEXAS INSTRUMENTS INCORPORATED

Inventor： Anshu JAIN ,  Kumar DESAPPAN

IPC: G06F7/499 ,  G06F7/483 ,  G06F5/01

Abstract: A technique for bit depth up-conversion including obtaining an input value for a computation in a first bit depth with a fewer number of bits as compared to a second bit depth, converting the input value from the first bit depth to the second bit depth as an unsigned data value, adjusting a pointer to the converted input value based on the first bit depth, performing the computation based on the adjusted pointer to obtain an adjusted output value, and performing a right shift operation on the adjusted output value based on the first bit depth to obtain an output value.

公开(公告)号：US20190295262A1

公开(公告)日：2019-09-26

申请号：US16157861

申请日：2018-10-11

Applicant: TEXAS INSTRUMENTS INCORPORATED

Inventor： Soyeb Noormohammed NAGORI ,  Manu MATHEW ,  Kumar DESAPPAN ,  Pramod Kumar SWAMI

IPC: G06T7/20 ,  G06T7/70

Abstract: A method for video object detection includes detecting an object in a first video frame, and selecting a first interest point and a second interest point of the object. The first interest point is in a first region of interest located at a first corner of a box surrounding the object. The second interest point is in a second region of interest located at a second corner of the box. The second corner is diagonally opposite the first corner. A first optical flow of the first interest point and a second optical flow of the second interest point are determined. A location of the object in a second video frame is estimated by determining, in the second video frame, a location of the first interest point based on the first optical flow and a location of the second interest point based on the second optical flow.

公开(公告)号：US20210056710A1

公开(公告)日：2021-02-25

申请号：US17093681

申请日：2020-11-10

Applicant: TEXAS INSTRUMENTS INCORPORATED

Inventor： Soyeb Noormohammed NAGORI ,  Manu MATHEW ,  Kumar DESAPPAN ,  Pramod Kumar SWAMI

IPC: G06T7/20 ,  G06T7/70

Abstract: A method for video object detection includes detecting an object in a first video frame, and selecting a first interest point and a second interest point of the object. The first interest point is in a first region of interest located at a first corner of a box surrounding the object. The second interest point is in a second region of interest located at a second corner of the box. The second corner is diagonally opposite the first corner. A first optical flow of the first interest point and a second optical flow of the second interest point are determined. A location of the object in a second video frame is estimated by determining, in the second video frame, a location of the first interest point based on the first optical flow and a location of the second interest point based on the second optical flow.

公开(公告)号：US20200272892A1

公开(公告)日：2020-08-27

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: G06N3/063 ,  G06N3/08 ,  G06F12/0804 ,  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：US20240320045A1

公开(公告)日：2024-09-26

申请号：US18675294

申请日：2024-05-28

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：US20230013998A1

公开(公告)日：2023-01-19

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 ,  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：US20220391776A1

公开(公告)日：2022-12-08

申请号：US17342037

申请日：2021-06-08

Applicant: Texas Instruments Incorporated

Inventor： Mihir Narendra MODY ,  Kumar DESAPPAN ,  Kedar Satish CHITNIS ,  Pramod Kumar SWAMI ,  Kevin Patrick LAVERY ,  Prithvi Shankar YEYYADI ANANTHA ,  Shyam JAGANNATHAN

IPC: G06N20/20 ,  G06F9/52 ,  G06F9/455

Abstract: Techniques for executing machine learning (ML) models including receiving an indication to run a ML model, receiving synchronization information for organizing the running of the ML model with other ML models, determining, based on the synchronization information, to delay running the ML model, delaying the running of the ML model, determining, based on the synchronization information, a time to run the ML model; and running the ML model at the time.

公开(公告)号：US20230064481A1

公开(公告)日：2023-03-02

申请号：US17463341

申请日：2021-08-31

Applicant: Texas Instruments Incorporated

Inventor： Tarkesh PANDE ,  Rishabh GARG ,  Pramod Kumar SWAMI ,  Kumar DESAPPAN ,  Aishwarya DUBEY

IPC: G06N20/00 ,  G06K9/62

Abstract: An electronic device, comprising one or more processors, wherein the one or more processors are configured to execute instructions causing the one or more processors to: receive a machine learning (ML) model and execution information associated with the ML model, wherein the execution information including first execution data indicating how to execute the ML model optimized based on a first performance criterion, and second execution data execution data indicating how to execute the ML model optimized based on a second performance criteria, the second performance criterion different from the first performance criteria; execute the ML model based on the first execution data; determine to execute the ML model based on the second execution data; and execute the ML model based on the second execution data.

公开(公告)号：US20230004855A1

公开(公告)日：2023-01-05

申请号：US17363856

申请日：2021-06-30

Applicant: Texas Instruments Incorporated

Inventor： Mihir Narendra MODY ,  Kumar DESAPPAN ,  Gregory Raymond SHURTZ ,  Jason A.T. JONES

IPC: G06N20/00 ,  G06F9/50

Abstract: Techniques for executing machine learning (ML) models including receiving an indication to execute an ML model on a processing core; determining a resource allocation for executing the ML model on the processing core; determining that a layer of the ML model will use a first amount of the resource, wherein the first amount is more than an amount of the resource allocated; determining that an adaptation may be applied to executing the layer of the ML model; executing the layer of the ML model using the adaptation, wherein executing the layer using the adaptation reduces the first amount of the resource used by the layer as compared to executing the layer without using the adaptation; and outputting a result of the ML model based on the executed layer.

公开(公告)号：US20220012635A1

公开(公告)日：2022-01-13

申请号：US17327869

申请日：2021-05-24

Applicant: Texas Instruments Incorporated

Inventor： Rishabh GARG ,  Pramod Kumar SWAMI ,  Kumar DESAPPAN ,  Anshu JAIN

IPC: G06N20/00 ,  G06K9/62 ,  G06F11/30

Abstract: Techniques for enhancing machine learning (ML) model execution. The technique includes determining an amount of memory used to process layers of a machine learning network having multiple layers, smoothing the amount of memory used to process the layers of the machine learning network based on a number of layers, identifying change layers where the smoothed amount of memory used changes more than a memory change threshold amount, grouping the layers of the machine learning network into a first layer grouping based on the identified change layers, and outputting the first layer grouping.