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公开(公告)号：US11720472B2
公开(公告)日：2023-08-08
申请号：US17522417
申请日：2021-11-09
Applicant: NVIDIA Corporation
Inventor： Richard Gavin Bramley , Philip Payman Shirvani , Nirmal R. Saxena
CPC classification number: G06F11/3471 , G06F11/073 , G06F11/0757 , G06F11/1068 , G06F11/3419 , G06F11/3457 , G06F11/3476 , G06F2201/81 , G06F2201/88
Abstract: Memory, used by a computer to store data, is generally prone to faults, including permanent faults (i.e. relating to a lifetime of the memory hardware), and also transient faults (i.e. relating to some external cause) which are otherwise known as soft errors. Since soft errors can change the state of the data in the memory and thus cause errors in applications reading and processing the data, there is a desire to characterize the degree of vulnerability of the memory to soft errors. In particular, once the vulnerability for a particular memory to soft errors has been characterized, cost/reliability trade-offs can be determined, or soft error detection mechanisms (e.g. parity) may be selectively employed for the memory. In some cases, memory faults can be diagnosed by redundant execution and a diagnostic coverage may be determined.
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公开(公告)号：US11592828B2
公开(公告)日：2023-02-28
申请号：US16745238
申请日：2020-01-16
Applicant: NVIDIA Corporation
Inventor： Richard Bramley , Philip Payman Shirvani , Nirmal Saxena
Abstract: In various examples, motifs, watermarks, and/or signature inputs are applied to a deep neural network (DNN) to detect faults in underlying hardware and/or software executing the DNN. Information corresponding to the motifs, watermarks, and/or signatures may be compared to the outputs of the DNN generated using the motifs, watermarks and/or signatures. When a the accuracy of the predictions are below a threshold, or do not correspond to the expected predictions of the DNN, the hardware and/or software may be determined to have a fault—such as a transient, an intermittent, or a permanent fault. Where a fault is determined, portions of the system that rely on the computations of the DNN may be shut down, or redundant systems may be used in place of the primary system. Where no fault is determined, the computations of the DNN may be relied upon by the system.
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公开(公告)号：US20180365017A1
公开(公告)日：2018-12-20
申请号：US15628403
申请日：2017-06-20
Applicant: NVIDIA Corporation
Inventor： Philip Payman Shirvani , Richard Gavin Bramley , John Montrym
IPC: G06F9/38
CPC classification number: G06F9/3863 , G06F9/3865 , G06F9/4812
Abstract: A method, computer readable medium, and system are disclosed for error coping. The method includes the steps of receiving, by a processing unit, a set of program instructions including a first program instruction that is responsive to error detection, detecting an error in a value of a first operand of the first program instruction, and determining that error coping execution is selectively enabled for the first instruction. The value for the first operand is replaced with a substitute value and the first program instruction is executed by the processing unit.
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公开(公告)号：US12271194B2
公开(公告)日：2025-04-08
申请号：US18157365
申请日：2023-01-20
Applicant: NVIDIA Corporation
Inventor： Richard Bramley , Philip Payman Shirvani , Nirmal Saxena
IPC: G05D1/00 , G06F18/2431 , G06N3/04 , G06V10/764 , G06V10/82 , G06V10/98 , G06V20/56
Abstract: In various examples, motifs, watermarks, and/or signature inputs are applied to a deep neural network (DNN) to detect faults in underlying hardware and/or software executing the DNN. Information corresponding to the motifs, watermarks, and/or signatures may be compared to the outputs of the DNN generated using the motifs, watermarks and/or signatures. When a the accuracy of the predictions are below a threshold, or do not correspond to the expected predictions of the DNN, the hardware and/or software may be determined to have a fault—such as a transient, an intermittent, or a permanent fault. Where a fault is determined, portions of the system that rely on the computations of the DNN may be shut down, or redundant systems may be used in place of the primary system. Where no fault is determined, the computations of the DNN may be relied upon by the system.
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公开(公告)号：US10691572B2
公开(公告)日：2020-06-23
申请号：US16115189
申请日：2018-08-28
Applicant: NVIDIA Corporation
Inventor： Richard Gavin Bramley , Philip Payman Shirvani , Nirmal R. Saxena
Abstract: Memory, used by a computer to store data, is generally prone to faults, including permanent faults (i.e. relating to a lifetime of the memory hardware), and also transient faults (i.e. relating to some external cause) which are otherwise known as soft errors. Since soft errors can change the state of the data in the memory and thus cause errors in applications reading and processing the data, there is a desire to characterize the degree of vulnerability of the memory to soft errors. In particular, once the vulnerability for a particular memory to soft errors has been characterized, cost/reliability trade-offs can be determined, or soft error detection mechanisms (e.g. parity) may be selectively employed for the memory. A method, computer readable medium, and system are provided for using liveness as a factor to evaluate memory vulnerability to soft errors.
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Patent Agency Ranking
公开(公告)号：US20190065338A1
公开(公告)日：2019-02-28
申请号：US16115189
申请日：2018-08-28
Applicant: NVIDIA Corporation
Inventor： Richard Gavin Bramley , Philip Payman Shirvani , Nirmal R. Saxena
CPC classification number: G06F11/3471 , G06F11/073 , G06F11/0757 , G06F11/1068 , G06F11/3419 , G06F11/3457 , G06F11/3476 , G06F2201/81 , G06F2201/88
Abstract: Memory, used by a computer to store data, is generally prone to faults, including permanent faults (i.e. relating to a lifetime of the memory hardware), and also transient faults (i.e. relating to some external cause) which are otherwise known as soft errors. Since soft errors can change the state of the data in the memory and thus cause errors in applications reading and processing the data, there is a desire to characterize the degree of vulnerability of the memory to soft errors. In particular, once the vulnerability for a particular memory to soft errors has been characterized, cost/reliability trade-offs can be determined, or soft error detection mechanisms (e.g. parity) may be selectively employed for the memory. A method, computer readable medium, and system are provided for using liveness as a factor to evaluate memory vulnerability to soft errors.
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公开(公告)号：US20220114075A1
公开(公告)日：2022-04-14
申请号：US17522417
申请日：2021-11-09
Applicant: NVIDIA Corporation
Inventor： Richard Gavin Bramley , Philip Payman Shirvani , Nirmal R. Saxena
Abstract: Memory, used by a computer to store data, is generally prone to faults, including permanent faults (i.e. relating to a lifetime of the memory hardware), and also transient faults (i.e. relating to some external cause) which are otherwise known as soft errors. Since soft errors can change the state of the data in the memory and thus cause errors in applications reading and processing the data, there is a desire to characterize the degree of vulnerability of the memory to soft errors. In particular, once the vulnerability for a particular memory to soft errors has been characterized, cost/reliability trade-offs can be determined, or soft error detection mechanisms (e.g. parity) may be selectively employed for the memory. In some cases, memory faults can be diagnosed by redundant execution and a diagnostic coverage may be determined.
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公开(公告)号：US20200293425A1
公开(公告)日：2020-09-17
申请号：US16849697
申请日：2020-04-15
Applicant: NVIDIA Corporation
Inventor： Richard Gavin Bramley , Philip Payman Shirvani , Nirmal R. Saxena
Abstract: Memory, used by a computer to store data, is generally prone to faults, including permanent faults (i.e. relating to a lifetime of the memory hardware), and also transient faults (i.e. relating to some external cause) which are otherwise known as soft errors. Since soft errors can change the state of the data in the memory and thus cause errors in applications reading and processing the data, there is a desire to characterize the degree of vulnerability of the memory to soft errors. In particular, once the vulnerability for a particular memory to soft errors has been characterized, cost/reliability trade-offs can be determined, or soft error detection mechanisms (e.g. parity) may be selectively employed for the memory. In some cases, memory faults can be diagnosed by redundant execution and a diagnostic coverage may be determined.
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9.发明授权公开(公告)号：US09292295B2
公开(公告)日：2016-03-22
申请号：US13914528
申请日：2013-06-10
Applicant: NVIDIA Corporation
Inventor： Philip Payman Shirvani , Peter Benjamin Sommers , Eric T. Anderson
CPC classification number: G06F9/3871
Abstract: A system, method, and computer program product for generating flow-control signals for a processing pipeline is disclosed. The method includes the steps of generating, by a first pipeline stage, a delayed ready signal based on a downstream ready signal received from a second pipeline stage and a throttle disable signal. A downstream valid signal is generated by the first pipeline stage based on an upstream valid signal and the delayed ready signal. An upstream ready signal is generated by the first pipeline stage based on the delayed ready signal and the downstream valid signal.
Abstract translation: 公开了一种用于产生处理流水线的流量控制信号的系统，方法和计算机程序产品。 该方法包括以下步骤：通过第一流水线级基于从第二流水线级接收到的下游就绪信号和节流阻止信号产生延迟就绪信号。 基于上行有效信号和延迟就绪信号，由第一流水线级产生下行有效信号。 基于延迟就绪信号和下行有效信号，由第一流水线级产生上行就绪信号。
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公开(公告)号：US20230152805A1
公开(公告)日：2023-05-18
申请号：US18157365
申请日：2023-01-20
Applicant: NVIDIA Corporation
Inventor： Richard Bramley , Philip Payman Shirvani , Nirmal Saxena
IPC: G05D1/00 , G05D1/02 , G06N3/04 , G06F18/2431 , G06V10/764 , G06V10/82 , G06V10/98 , G06V20/56
CPC classification number: G05D1/0088 , G05D1/0221 , G06N3/04 , G05D1/0246 , G06F18/2431 , G06V10/764 , G06V10/82 , G06V10/98 , G06V20/56
Abstract: In various examples, motifs, watermarks, and/or signature inputs are applied to a deep neural network (DNN) to detect faults in underlying hardware and/or software executing the DNN. Information corresponding to the motifs, watermarks, and/or signatures may be compared to the outputs of the DNN generated using the motifs, watermarks and/or signatures. When a the accuracy of the predictions are below a threshold, or do not correspond to the expected predictions of the DNN, the hardware and/or software may be determined to have a fault—such as a transient, an intermittent, or a permanent fault. Where a fault is determined, portions of the system that rely on the computations of the DNN may be shut down, or redundant systems may be used in place of the primary system. Where no fault is determined, the computations of the DNN may be relied upon by the system.
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