公开(公告)号：US11934932B1

公开(公告)日：2024-03-19

申请号：US17094598

申请日：2020-11-10

Applicant: XILINX, INC.

Inventor： Giulio Gambardella ,  Nicholas Fraser ,  Ussama Zahid ,  Michaela Blott ,  Kornelis A. Vissers

IPC: G06N20/20 ,  G06F11/16 ,  G06N3/045 ,  G06N3/08

CPC classification number: G06N20/20 ,  G06F11/16 ,  G06N3/045 ,  G06N3/08

Abstract: Examples herein propose operating redundant ML models which have been trained using a boosting technique that considers hardware faults. The embodiments herein describe performing an evaluation process where the performance of a first ML model is measured in the presence of a hardware fault. The errors introduced by the hardware fault can then be used to train a second ML model. In one embodiment, a second evaluation process is performed where the combined performance of both the first and second trained ML models is measured in the presence of a hardware fault. The resulting errors can then be used when training a third ML model. In this manner, the three trained ML models are trained to be error aware. As a result, during operation, if a hardware fault occurs, the three ML models have better performance relative to three ML models that where not trained to be error aware.