公开(公告)号：US20180349096A1

公开(公告)日：2018-12-06

申请号：US15995647

申请日：2018-06-01

Applicant: TEXAS INSTRUMENTS INCORPORATED

Inventor： Arthur John REDFERN ,  Asheesh BHARDWAJ ,  Tarek Aziz LAHLOU ,  William Franklin LEVEN

IPC: G06F7/36 ,  G06F9/30 ,  G06F7/24 ,  G06F7/32

Abstract: A merge sort accelerator (MSA) includes a pre-processing stage configured to receive an input vector and generate a pre-processing output vector based on a pre-processing instruction and the input vector. The MSA also includes a merge sort network having multiple sorting stages configured to be selectively enabled. The merge sort network is configured to receive the pre-processing output vector and generate a sorted output vector based on a sorting instruction and the pre-processing output vector. The MSA includes an accumulator stage configured to receive the sorted output vector and update an accumulator vector based on the accumulator instruction and the sorted output vector. The MSA also includes a post-processing stage configured to receive the accumulator vector and generate a post-processing output vector based on a post-processing instruction and the accumulator vector.

公开(公告)号：US20230237368A1

公开(公告)日：2023-07-27

申请号：US17585197

申请日：2022-01-26

Applicant: TEXAS INSTRUMENTS INCORPORATED

Inventor： Arthur John REDFERN ,  Lijun ZHU ,  Molly Katherine NEWQUIST

IPC: G06N20/00 ,  G06F17/15

CPC classification number: G06N20/00 ,  G06F17/15

Abstract: Techniques for a machine learning model including the steps of summing values of a set of non-binary input feature values with bias values of a first set of bias values to generate first summed values; binarizing the first summed values; receiving a set of binary weights; performing a convolution operation on the binarized summed values and the set of binary weights to generate convolved output feature values; summing feature values of the convolved output feature values with bias values of a second set of bias values and applying a scale value of a first set of scale values to generate a first set of normalized feature values; summing the first set of normalized feature values with the non-binary input feature values to generate second summed values; and outputting a set of output feature values based on the second summed normalized feature values and non-binary input feature values.