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.

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.

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.

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).

Abstract: A method and a controller for controlling a video processing unit to facilitate detection of newcomers in a first environment. The method comprises: capturing a thermal image of a human object in the first environment, the first environment being associated with a first climate; calculating, based on the thermal image, a thermal signature of a portion of the human object; determining that the human object has entered the first environment from a second environment when the thermal signature of the portion of the human object deviates from a predetermined thermal signature associated with the first environment, wherein the second environment is associated with a second, different, climate; and controlling the video processing unit to prioritize the human object over other human objects when processing video frames depicting the human object together with the other human objects.

Abstract: A method and a controller for controlling a video processing unit to facilitate detection of newcomers in a first environment. The method comprises: capturing a thermal image of a human object in the first environment, the first environment being associated with a first climate; calculating, based on the thermal image, a thermal signature of a portion of the human object; determining that the human object has entered the first environment from a second environment when the thermal signature of the portion of the human object deviates from a predetermined thermal signature associated with the first environment, wherein the second environment is associated with a second, different, climate; and controlling the video processing unit to prioritize the human object over other human objects when processing video frames depicting the human object together with the other human objects.