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公开(公告)号：US08949518B2
公开(公告)日：2015-02-03
申请号：US14095035
申请日：2013-12-03
Applicant: Apple Inc.
Inventor： Umesh S. Vaishampayan , Daniel A. Chimene , Daniel S. Heller
CPC classification number: G06F12/0292 , G06F11/073 , G06F11/076 , G06F11/079 , G06F11/3466 , G06F12/0253
Abstract: Techniques for tracking memory usages of a data processing system are described herein. According to one embodiment, a memory manager is to perform a first lookup operation in a memory allocation table to identify an allocation entry based on a handle representing a memory address of a memory block allocated to a client and to retrieve a trace entry pointer from the allocation entry. The memory manager is then to perform a second lookup operation in a memory trace table to identify a trace entry based on the trace entry pointer and to increment a memory allocation count of the trace entry. The memory allocation count is utilized to indicate a likelihood of the client causing a memory leak.
Abstract translation: 本文描述了用于跟踪数据处理系统的存储器使用的技术。 根据一个实施例，存储器管理器将在存储器分配表中执行第一查找操作，以基于表示分配给客户端的存储器块的存储器地址的句柄来识别分配条目，并从存储器分配表中检索跟踪条目指针 分配条目。 然后，存储器管理器在存储器跟踪表中执行第二查找操作，以基于跟踪条目指针识别跟踪条目，并增加跟踪条目的存储器分配计数。 存储器分配计数用于指示客户端造成内存泄漏的可能性。
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公开(公告)号：US12175375B2
公开(公告)日：2024-12-24
申请号：US17903991
申请日：2022-09-06
Applicant: Apple Inc.
Inventor： Gaurav Kapoor , Cecile M. Foret , Francesco Rossi , Kit-Man Wan , Umesh S. Vaishampayan , Etienne Belanger , Albert Antony , Alexey Marinichev , Marco Zuliani , Xiaojin Shi
Abstract: The subject technology provides receiving a neural network (NN) model to be executed on a target platform, the NN model including multiple layers that include operations and some of the operations being executable on multiple processors of the target platform. The subject technology further sorts the operations from the multiple layers in a particular order based at least in part on grouping the operations that are executable by a particular processor of the multiple processors. The subject technology determines, based at least in part on a cost of transferring the operations between the multiple processors, an assignment of one of the multiple processors for each of the sorted operations of each of the layers in a manner that minimizes a total cost of executing the operations. Further, for each layer of the NN model, the subject technology includes an annotation to indicate the processor assigned for each of the operations.
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公开(公告)号：US12051006B2
公开(公告)日：2024-07-30
申请号：US17903991
申请日：2022-09-06
Applicant: Apple Inc.
Inventor： Gaurav Kapoor , Cecile M. Foret , Francesco Rossi , Kit-Man Wan , Umesh S. Vaishampayan , Etienne Belanger , Albert Antony , Alexey Marinichev , Marco Zuliani , Xiaojin Shi
CPC classification number: G06N3/10 , G06F8/41 , G06F8/443 , G06F8/4441 , G06N3/04 , G06N3/063 , G06N3/08 , G06F9/50 , G06N3/08 , G06N3/063 , G06N3/04 , G06N3/10
Abstract: The subject technology provides receiving a neural network (NN) model to be executed on a target platform, the NN model including multiple layers that include operations and some of the operations being executable on multiple processors of the target platform. The subject technology further sorts the operations from the multiple layers in a particular order based at least in part on grouping the operations that are executable by a particular processor of the multiple processors. The subject technology determines, based at least in part on a cost of transferring the operations between the multiple processors, an assignment of one of the multiple processors for each of the sorted operations of each of the layers in a manner that minimizes a total cost of executing the operations. Further, for each layer of the NN model, the subject technology includes an annotation to indicate the processor assigned for each of the operations.
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公开(公告)号：US11501008B2
公开(公告)日：2022-11-15
申请号：US16938741
申请日：2020-07-24
Applicant: Apple Inc.
Inventor： Abhishek Bhowmick , Andrew H. Vyrros , Matthew R. Salesi , Umesh S. Vaishampayan
Abstract: Embodiments described herein ensure differential privacy when transmitting data to a server that estimates a frequency of such data amongst a set of client devices. The differential privacy mechanism may provide a predictable degree of variance for frequency estimations of data. The system may use a multibit histogram model or Hadamard multibit model for the differential privacy mechanism, both of which provide a predictable degree of accuracy of frequency estimations while still providing mathematically provable levels of privacy.
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公开(公告)号：US11496600B2
公开(公告)日：2022-11-08
申请号：US16554518
申请日：2019-08-28
Applicant: Apple Inc.
Inventor： Umesh S. Vaishampayan , Gaurav Kapoor , Kit-man Wan
IPC: H04L67/59 , H04L67/2895 , G06F3/04883 , G06N20/00 , H04W4/021 , H04L67/00
Abstract: In an exemplary process for remote execution of machine-learned models, one or more signals from a second electronic device is detected by a first electronic device. The second electronic device includes a machine-learned model associated with an application implemented on the first electronic device. Based on the one or more signals, a communication connection is established with the second electronic device and a proxy to the machine-learned model is generated. Input data is obtained via a sensor of the first electronic device. A representation of the input data is sent to the second electronic device via the proxy and the established communication connection. The representation of the input data is processed through the machine-learned model to generate an output. A result derived from the output is received via the communication connection and a representation of the result is outputted.
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Patent Agency Ranking
公开(公告)号：US11227063B2
公开(公告)日：2022-01-18
申请号：US17020637
申请日：2020-09-14
Applicant: Apple Inc.
Inventor： Abhishek Bhowmick , Andrew H. Vyrros , Umesh S. Vaishampayan , Kevin W. Decker , Conrad Shultz , Steve Falkenburg , Mateusz Rajca
IPC: H04L29/06 , G06F21/62 , H04L9/32 , H04L29/08 , H04W12/02 , G06F3/0483 , G06F1/28 , G06F3/0482
Abstract: Embodiments described herein provide a privacy mechanism to protect user data when transmitting the data to a server that estimates a frequency of such data amongst a set of client devices. In one embodiment, a differential privacy mechanism is implemented using a count-mean-sketch technique that can reduce resource requirements required to enable privacy while providing provable guarantees regarding privacy and utility. For instance, the mechanism can provide the ability to tailor utility (e.g. accuracy of estimations) against the resource requirements (e.g. transmission bandwidth and computation complexity).
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公开(公告)号：US10599868B2
公开(公告)日：2020-03-24
申请号：US15805632
申请日：2017-11-07
Applicant: Apple Inc.
Inventor： Gavin Barraclough , Christophe Dumez , Abhishek Bhowmick , Andrew H. Vyrros , Umesh S. Vaishampayan
IPC: G06F7/04 , G06F21/62 , H04L29/06 , H04L9/32 , H04L29/08 , H04W12/02 , G06F3/0483 , G06F1/28 , G06F3/0482
Abstract: Embodiments described herein provide a privacy mechanism to protect user data when transmitting the data to a server that estimates a frequency of such data amongst a set of client devices. One embodiment uses a differential privacy mechanism to enhance a user experience by identifying particular websites that exhibit particular characteristics. In one embodiment, websites that are associated with a high resource consumption are identified. High resource consumption can be identified based on threshold of particular resources such as processor, memory, network bandwidth, and power usage.
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公开(公告)号：US10594835B2
公开(公告)日：2020-03-17
申请号：US15151338
申请日：2016-05-10
Applicant: Apple Inc.
Inventor： Alexander Barraclough Brown , Umesh S. Vaishampayan
IPC: G06F15/177 , H04L29/08 , H04W24/02 , G06F9/445 , G06F11/30 , G06F9/46 , G06N7/00 , G06F8/61 , G06F9/54 , H04W52/02
Abstract: Disclosed are systems, methods, and non-transitory computer-readable storage media for efficiently monitoring the operating context of a computing device. In some implementations, the context daemon and/or the context client can be terminated to conserve system resources. For example, if the context daemon and/or the context client are idle, they can be shutdown to conserve battery power or free other system resources (e.g., memory). When an event occurs (e.g., a change in current context) that requires the context daemon and/or the context client to be running, the context daemon and/or the context client can be restarted to handle the event. Thus, system resources can be conserved while still providing relevant context information collection and callback notification features.
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公开(公告)号：US10552631B2
公开(公告)日：2020-02-04
申请号：US16297464
申请日：2019-03-08
Applicant: Apple Inc.
Inventor： Yannick L. Sierra , Abhradeep Guha Thakurta , Umesh S. Vaishampayan , John C. Hurley , Keaton F. Mowery , Michael Brouwer
Abstract: One embodiment provides a system that implements a 1-bit protocol for differential privacy for a set of client devices that transmit information to a server. Implementations may leverage specialized instruction sets or engines built into the hardware or firmware of a client device to improve the efficiency of the protocol. For example, a client device may utilize these cryptographic functions to randomize information sent to the server. In one embodiment, the client device may use cryptographic functions such as hashes including SHA or block ciphers including AES to provide an efficient mechanism for implementing differential privacy.
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公开(公告)号：US20190097978A1
公开(公告)日：2019-03-28
申请号：US16159473
申请日：2018-10-12
Applicant: Apple Inc.
Inventor： Abhradeep Guha Thakurta , Andrew H. Vyrros , Umesh S. Vaishampayan , Gaurav Kapoor , Julien Freudiger , Vivek Rangarajan Sridhar , Doug Davidson
Abstract: Systems and methods are disclosed for a server learning new words generated by user client devices in a crowdsourced manner while maintaining local differential privacy of client devices. A client device can determine that a word typed on the client device is a new word that is not contained in a dictionary or asset catalog on the client device. New words can be grouped in classifications such as entertainment, health, finance, etc. A differential privacy system on the client device can comprise a privacy budget for each classification of new words. If there is privacy budget available for the classification, then one or more new terms in a classification can be sent to new term learning server, and the privacy budget for the classification reduced. The privacy budget can be periodically replenished.
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