公开(公告)号：US11996527B2

公开(公告)日：2024-05-28

申请号：US17541000

申请日：2021-12-02

Applicant: Arm Limited

Inventor： Emre Ozer ,  Remy Pottier ,  Jedrzej Kufel ,  John Philip Biggs ,  James Edward Myers

IPC: H01M10/48 ,  G01R31/392

CPC classification number: H01M10/482 ,  G01R31/392

Abstract: A battery cell monitoring system comprises a flexible substrate able to conform to a surface of a battery cell to be monitored, and a plurality of first-level prediction units integrated onto the flexible substrate, where each first-level prediction unit is positioned at a different location on the flexible substrate to each other first-level prediction unit. Each first-level prediction unit comprises at least one sensor to generate sensor signals indicative of a physical state of the battery cell, and first-level prediction circuitry to generate a predicted battery cell status value in dependence on the sensor signals received from the at least one sensor of that first-level prediction unit. Second-level prediction circuitry is arranged to determine a prediction result in dependence on the predicted battery cell status values generated by the first-level prediction circuitry of each first-level prediction unit, and a communications device is used to output the prediction result at least when the prediction result indicates an occurrence of a critical event.

公开(公告)号：US10810094B2

公开(公告)日：2020-10-20

申请号：US16014154

申请日：2018-06-21

Applicant: Arm Limited

Inventor： Milosch Meriac ,  Xabier Iturbe ,  Emre Ozer ,  Balaji Venu ,  Shidhartha Das

IPC: G06F11/00 ,  G06F11/20 ,  G06F11/16 ,  G06F11/14 ,  G06F11/30 ,  G06F21/50

Abstract: Examples of the present disclosure relate to a method for anomaly response in a system on chip. The method comprises measuring a magnitude of a transient anomaly event in an operating condition of the system on chip. Based on the magnitude it is determined, for each of a plurality of components of the system on chip, an indication of susceptibility of that component to an anomaly event of the measured magnitude. Based on the determined indications of susceptibility for each of the plurality of components, an anomaly response action is determined. The method then comprises performing the anomaly response action.

公开(公告)号：US20140115376A1

公开(公告)日：2014-04-24

申请号：US14143065

申请日：2013-12-30

Applicant: ARM Limited

Inventor： Shidhartha DAS ,  David Michael Bull ,  Emre Ozer

IPC: G06F11/07

CPC classification number: G06F11/0793 ,  G01R31/31816 ,  G06F11/1076 ,  G06F11/1608

Abstract: An integrated circuit is provided with error detection circuitry and error repair circuitry. Error tolerance circuitry is responsive to a control parameter to selectively disable the error repair circuitry. The control parameter is dependent on the processing performed within the circuit. For example, the control parameter may be generated in dependence upon the program instruction being executed, the output signal value which is in error, the previous behavior of the circuit or in other ways.

公开(公告)号：US11969030B2

公开(公告)日：2024-04-30

申请号：US17543070

申请日：2021-12-06

Applicant: Arm Limited

Inventor： Emre Ozer ,  Jedrzej Kufel ,  James Edward Myers ,  Remy Pottier ,  John Philip Biggs

IPC: A41D1/00 ,  A41D19/015 ,  A61F6/04 ,  G01R31/28

CPC classification number: A41D1/002 ,  A41D19/015 ,  A61F6/04 ,  G01R31/2812

Abstract: Wearable items and methods of monitoring wearable items are disclosed. The wearable item comprises a flexible base material forming at least a portion of the wearable item, plural conductive traces traversing the flexible base material, and conductivity sensing circuitry coupled to the plural conductive traces. The conductivity sensing circuitry is configured to distinguish conductivity from non-conductivity of the plural conductive traces, and configured to generate a conductivity indication for at least one of the plural conductive traces. The plural conductive traces follow indirect paths across the flexible base material, allowing the flexible material to flex and stretch normally without breaking the conductive traces.

公开(公告)号：US11061852B2

公开(公告)日：2021-07-13

申请号：US16645993

申请日：2018-09-25

Applicant: Arm Limited

Inventor： Mbou Eyole ,  Emre Ozer ,  Xabier Iturbe ,  Shidhartha Das

IPC: G11C7/00 ,  G06F15/78 ,  G11C14/00 ,  H03K19/17728 ,  G06F15/76

Abstract: A method of reconfiguration and a reconfigurable circuit architecture comprising a configurable volatile storage circuit and Non-Volatile Memory circuit elements; wherein the Non-Volatile memory circuit elements store multiple bit states for re-configuration, the multiple bit states being read from the Non-Volatile memory circuit elements and written into the configurable volatile storage circuit for reconfiguration. The Non-Volatile Memory circuit elements and the configurable volatile storage circuit are provided on a common die.

公开(公告)号：US20210138464A1

公开(公告)日：2021-05-13

申请号：US17052990

申请日：2019-05-07

Applicant: Arm Limited

Inventor： Emre Ozer ,  Milosch Meriac ,  Hugo John Martin Vincent

IPC: B01L3/00 ,  G01N15/06

Abstract: A fluid delivery control device comprising; (i) at least one inlet portal to allow fluid passage into a chamber; (ii) at least one outlet portal to allow fluid passage from the chamber; (iii) at least one biosensor; (iv) at least one actuator; and wherein the at least one biosensor is in fluid communication with said fluid and is associated with a valve having actuator capability, the valve having actuator capability being in communication with sensor measured conditions upon which the valve permits or inhibits delivery of the fluid from the chamber.

公开(公告)号：US20210125097A1

公开(公告)日：2021-04-29

申请号：US16665255

申请日：2019-10-28

Applicant: Arm Limited

Inventor： Emre Ozer

IPC: G06N20/00 ,  G06K9/62 ,  G06F16/35 ,  H04N19/176 ,  H04N19/61 ,  G06N3/08

Abstract: A safety-based prediction apparatus, system and method are provided. A machine learning hardware accelerator (MLHA) includes a main classifier (MC) module, at least one guardian classifier (GC) module, and a final predicted class decision module. The MC module predicts an MC predicted class based on input data, and includes a pre-trained, machine learning main classifier (MLMC) that has at least one safety critical (SC) class and a plurality of non-SC classes. Each guardian classifier (GC) module is associated with an SC class, and predicts a GC predicted class based on the input data. Each GC module includes a pre-trained, machine learning guardian classifier (MLGC) having two classes including an associated SC class and a residual class that includes any non-associated SC classes and the plurality of non-SC classes. A decision module determines and outputs a final predicted class based on the MC predicted class and each GC predicted class.

公开(公告)号：US12093121B2

公开(公告)日：2024-09-17

申请号：US17547963

申请日：2021-12-10

Applicant: Arm Limited

Inventor： Emre Ozer ,  Mbou Eyole ,  Jedrzej Kufel ,  John Philip Biggs

IPC: G06F11/07 ,  G06F13/24 ,  G06F30/20

CPC classification number: G06F11/0787 ,  G06F13/24 ,  G06F30/20

Abstract: Methods of performing post-manufacturing adaptation of a data processing apparatus manufactured in accordance with a processor design and corresponding data processing apparatus configurations are provided. Post-manufacturing testing of the data processing apparatus determines any dysfunctional instructions by comparison between component usage profiles for each instruction and a component fault-detection procedure applied to the data processing apparatus. The data processing apparatus can be determined nevertheless to be operationally viable when any dysfunctional instructions can be substituted for by emulation using other functional instructions. The data processing apparatus can be provided with dysfunctional instruction handling circuitry configured to identify occurrence of a program instruction instance of a dysfunctional instruction and to invoke an interrupt handling routine associated with the dysfunctional instruction to emulate the instance of a dysfunctional instruction.

公开(公告)号：US11514362B2

公开(公告)日：2022-11-29

申请号：US16665255

申请日：2019-10-28

Applicant: Arm Limited

Inventor： Emre Ozer

IPC: G06V10/00 ,  G06N20/00 ,  H04N19/176 ,  H04N19/61 ,  G06F16/35 ,  G06K9/62 ,  G06N3/08

Abstract: A safety-based prediction apparatus, system and method are provided. A machine learning hardware accelerator (MLHA) includes a main classifier (MC) module, at least one guardian classifier (GC) module, and a final predicted class decision module. The MC module predicts an MC predicted class based on input data, and includes a pre-trained, machine learning main classifier (MLMC) that has at least one safety critical (SC) class and a plurality of non-SC classes. Each guardian classifier (GC) module is associated with an SC class, and predicts a GC predicted class based on the input data. Each GC module includes a pre-trained, machine learning guardian classifier (MLGC) having two classes including an associated SC class and a residual class that includes any non-associated SC classes and the plurality of non-SC classes. A decision module determines and outputs a final predicted class based on the MC predicted class and each GC predicted class.

公开(公告)号：US20200218625A1

公开(公告)日：2020-07-09

申请号：US16823180

申请日：2020-03-18

Applicant: Arm Limited

Inventor： Emre Ozer ,  Xabier Iturbe ,  Balaji Venu

IPC: G06F11/27 ,  G06F11/22 ,  G06F11/16

Abstract: Briefly, example methods, apparatuses, and/or articles of manufacture are disclosed that may be implemented, in whole or in part, to determine indicators of potential errors in a multi-processing core lockstep computing device comprising a plurality of processing cores, based, at least in part, on observations of output signals generated by at least two processing cores of the plurality of processing cores. A built-in self-test (BIST) procedure may then be based, at least in part, on the determining indicators.