公开(公告)号：US20210224658A1

公开(公告)日：2021-07-22

申请号：US17117271

申请日：2020-12-10

Applicant: Texas Instruments Incorporated

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

IPC: G06N3/08 ,  G06N3/04

Abstract: In described examples of a method for quantizing data for a convolutional neural network (CNN) is provided. A set of data is received and quantized the using a power-of-2 parametric activation (PACT2) function. The PACT2 function arranges the set of data as a histogram and discards a portion of the data corresponding to a tail of the histogram to form a remaining set of data. A clipping value is determined by expanding the remaining set of data to a nearest power of two value. The set of data is then quantized using the clipping value. With PACT2, a model can be quantized either using post training quantization or using quantization aware training. PACT2 helps a quantized model to achieve close accuracy compared to the corresponding floating-point model.

公开(公告)号：US20210112257A1

公开(公告)日：2021-04-15

申请号：US17106954

申请日：2020-11-30

Applicant: TEXAS INSTRUMENTS INCORPORATED

Inventor： Yashwant Dutt ,  Kumar Desappan ,  Piyali Goswami

IPC: H04N19/167 ,  H04N19/103 ,  H04N19/176 ,  H04N19/157 ,  H04N19/124

Abstract: Several methods and systems for masking multimedia data are disclosed. In an embodiment, a method for masking includes performing a prediction for at least one multimedia data block based on a prediction mode of a plurality of prediction modes. The at least one multimedia data block is associated with a region of interest (ROI). A residual multimedia data associated with the at least one multimedia data block is generated based on the prediction. A quantization of the residual multimedia data is performed based on a quantization parameter (QP) value. The QP value is variable such that varying the QP value controls a degree of masking of the ROI.

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：US20160191923A1

公开(公告)日：2016-06-30

申请号：US15063234

申请日：2016-03-07

Applicant: Texas Instruments Incorporated

Inventor： Yashwant Dutt ,  Kumar Desappan ,  Piyali Goswami

IPC: H04N19/167 ,  H04N19/176 ,  H04N19/157 ,  H04N19/103 ,  H04N19/124

CPC classification number: H04N19/167 ,  H04N19/103 ,  H04N19/124 ,  H04N19/157 ,  H04N19/176

Abstract: Several methods and systems for masking multimedia data are disclosed. In an embodiment, a method for masking includes performing a prediction for at least one multimedia data block based on a prediction mode of a plurality of prediction modes. The at least one multimedia data block is associated with a region of interest (ROI). A residual multimedia data associated with the at least one multimedia data block is generated based on the prediction. A quantization of the residual multimedia data is performed based on a quantization parameter (QP) value. The QP value is variable such that varying the QP value controls a degree of masking of the ROI.

Abstract translation: 公开了用于掩蔽多媒体数据的几种方法和系统。 在一个实施例中，掩蔽方法包括基于多种预测模式的预测模式对至少一个多媒体数据块进行预测。 所述至少一个多媒体数据块与感兴趣区域（ROI）相关联。 基于该预测，生成与该至少一个多媒体数据块相关联的剩余多媒体数据。 基于量化参数（QP）值来执行残余多媒体数据的量化。 QP值是可变的，使得改变QP值控制ROI的掩蔽程度。