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公开(公告)号：US20230065730A1
公开(公告)日：2023-03-02
申请号：US17863094
申请日：2022-07-12
Applicant: Axis AB
Inventor： Anton JAKOBSSON
Abstract: A method of operating a hardware accelerator comprises: implementing a multi-layer neural network using the hardware accelerator; measuring a power consumption of the hardware accelerator while executing a predefined operation on the multi-layer network at a default clock frequency; evaluating one or more power management criteria for the measured power consumption; and, in response to exceeding one of the power management criteria, deciding to reduce the clock frequency relative to the default clock frequency. In the step of measuring a power consumption of the hardware accelerator, per-layer measurements which each relate to fewer than all layers of the neural network may be captured.
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2.发明申请公开(公告)号：US20220171978A1
公开(公告)日：2022-06-02
申请号：US17510407
申请日：2021-10-26
Applicant: Axis AB
Inventor： Björn ARDÖ , Anton JAKOBSSON
Abstract: A method (100), a device (600;700) and a system (800) for processing image data representing a scene for extracting features related to objects in the scene using a convolutional neural network are disclosed. Two or more portions of the image data representing a respective one of two or more portions of the scene are processed (S110), by means of a respective one of two or more circuitries, through a first number of layers of the convolutional neural network to form two or more outputs, wherein the two or more portions of the scene are partially overlapping. The two or more outputs are combined (S120) to form a combined output, and the combined output is processed (S130) through a second number of layers of the convolutional neural network by means of one of the two or more circuitries for extracting features related to objects in the scene.
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3.发明申请公开(公告)号：US20210350129A1
公开(公告)日：2021-11-11
申请号：US17224610
申请日：2021-04-07
Applicant: Axis AB
Inventor： Andreas MUHRBECK , Anton JAKOBSSON , Niclas SVENSSON
Abstract: Methods and apparatus, including computer program products, for processing images recorded by a camera (202) monitoring a scene (200). A set of images (204, 206, 208) is received. The set of images (204, 206, 208) includes differently exposed images of the scene (200) recorded by the camera (202). The set of images (204, 206, 208) is processed by a trained neural network (210) configured to perform object detection, object classification and/or object recognition in image data, wherein the neural network (210) uses image data from at least two differently exposed images in the set of images (204, 206, 208) to detect objects in the set of images (204, 206, 208).
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