公开(公告)号：US20170364792A1

公开(公告)日：2017-12-21

申请号：US15625578

申请日：2017-06-16

申请人： SRI International

发明人： Sek M. Chai ,  David C. Zhang ,  Mohamed R. Amer ,  Timothy J. Shields ,  Aswin Nadamuni Raghavan ,  Bhaskar Ramamurthy

IPC分类号： G06N3/04 ,  G06N3/08 ,  G06N3/063

CPC分类号： G06N3/0454 ,  G06F9/46 ,  G06F9/50 ,  G06N3/0445 ,  G06N3/063 ,  G06N3/08

摘要： Operations of computing devices are managed using one or more deep neural networks (DNNs), which may receive, as DNN inputs, data from sensors, instructions executed by processors, and/or outputs of other DNNs. One or more DNNs, which may be generative, can be applied to the DNN inputs to generate DNN outputs based on relationships between DNN inputs. The DNNs may include DNN parameters learned using one or more computing workloads. The DNN outputs may be, for example, control signals for managing operations of computing devices, predictions for use in generating control signals, warnings indicating an acceptable state is predicted, and/or inputs to one or more neural networks. The signals enhance performance, efficiency, and/or security of one or more of the computing devices. DNNs can be dynamically trained to personalize operations by updating DNN weights or other parameters.

公开(公告)号：US11328206B2

公开(公告)日：2022-05-10

申请号：US15625578

申请日：2017-06-16

申请人： SRI International

发明人： Sek M. Chai ,  David C. Zhang ,  Mohamed R. Amer ,  Timothy J. Shields ,  Aswin Nadamuni Raghavan ,  Bhaskar Ramamurthy

IPC分类号： G06N3/04 ,  G06N3/063 ,  G06N3/08 ,  G06F9/46 ,  G06F9/50

摘要： Operations of computing devices are managed using one or more deep neural networks (DNNs), which may receive, as DNN inputs, data from sensors, instructions executed by processors, and/or outputs of other DNNs. One or more DNNs, which may be generative, can be applied to the DNN inputs to generate DNN outputs based on relationships between DNN inputs. The DNNs may include DNN parameters learned using one or more computing workloads. The DNN outputs may be, for example, control signals for managing operations of computing devices, predictions for use in generating control signals, warnings indicating an acceptable state is predicted, and/or inputs to one or more neural networks. The signals enhance performance, efficiency, and/or security of one or more of the computing devices. DNNs can be dynamically trained to personalize operations by updating DNN weights or other parameters.