Abstract: An artificial intelligence processor can optimize the usage of its neural network to process a data set more efficiently. The artificial intelligence processor can have a neural network of multiple arithmetic logic units each having one or more computing engines and a local arithmetic memory divided into a set of clusters arranged into a node ring. A scheduler with a local scheduler memory for each cluster. An advanced extensible interface can read a data set model from an external memory in a single data read. A memory manager can control the node ring. When a data size of the data set is larger than a processing model layer for processing the data set, the memory manager can slice the data set into data set chunks. The memory manager can assign a data set chunk to a data cluster. The memory manager can broadcast channel instructions from the processing model layer to every cluster.

Abstract: An integrated circuit with a neural network can reduce the number of accesses off circuit by embedding a dedicated processor for each cluster in a neural network. The integrated circuit has a neural network of multiple arithmetic logic units arranged in clusters. Each arithmetic logic unit have one or more computing engines and a local arithmetic memory. The integrated circuit can associate a scheduler with each cluster. The integrated circuit can associate a cluster local memory with each cluster. The integrated circuit can associate a dedicated embedded processor with each cluster. The dedicated embedded processor is capable of performing general purpose operations. The integrated circuit can execute a non-computational operation offloaded from the cluster.

Abstract: The technology relates to determining speaker positioning in a multichannel sound system. In one example, one or more controllers perform time of flight measurements between one or more pairs of speakers of the plurality of speakers. A distance between each of the one or more pairs of speakers based on the time of flight measurements is determined A location of each speaker in the one or more pairs of speakers relative to the other speakers in the one or more pairs of speakers is determined. Based on the determined location of each speaker the multichannel sound system is calibrated.

Abstract: Systems and methods discussed herein can provide three-dimensional audio virtualization with sweet spot adaptation. In an example, an audio processor circuit can be used to update audio signals for sweet spot adaptation based on information from at least one depth sensor or camera about a listener position in a listening environment.

Abstract: Systems and methods discussed herein can provide three-dimensional audio virtualization with sweet spot adaptation. In an example, an audio processor circuit can be used to update audio signals for sweet spot adaptation based on information from at least one depth sensor or camera about a listener position in a listening environment.

Abstract: Systems and methods discussed herein can provide three-dimensional audio virtualization with sweet spot adaptation. In an example, an audio processor circuit can be used to update audio signals for sweet spot adaptation based on user information input to a graphical user interface information about a listener position in a listening environment.

Abstract: Systems and methods discussed herein can provide three-dimensional audio virtualization with sweet spot adaptation. In an example, an audio processor circuit can be used to update audio signals for sweet spot adaptation based on user information input to a graphical user interface information about a listener position in a listening environment.

Abstract: Systems and methods discussed herein can provide three-dimensional audio virtualization with sweet spot adaptation. In an example, an audio processor circuit can be used to update audio signals for sweet spot adaptation based on information from at least one depth sensor or camera about a listener position in a listening environment.

Abstract: A template matching module is configured to program a processor to apply multiple differently-tuned object detection classifier sets in parallel to a digital image to determine one or more of an object type, configuration, orientation, pose or illumination condition, and to dynamically switch between object detection templates to match a determined object type, configuration, orientation, pose, blur, exposure and/or directional illumination condition.

Abstract: A template matching module is configured to program a processor to apply multiple differently-tuned object detection classifier sets in parallel to a digital image to determine one or more of an object type, configuration, orientation, pose or illumination condition, and to dynamically switch between object detection templates to match a determined object type, configuration, orientation, pose, blur, exposure and/or directional illumination condition.

Abstract: A template matching module is configured to program a processor to apply multiple differently-tuned object detection classifier sets in parallel to a digital image to determine one or more of an object type, configuration, orientation, pose or illumination condition, and to dynamically switch between object detection templates to match a determined object type, configuration, orientation, pose, blur, exposure and/or directional illumination condition.

Abstract: A template matching module is configured to program a processor to apply multiple differently-tuned object detection classifier sets in parallel to a digital image to determine one or more of an object type, configuration, orientation, pose or illumination condition, and to dynamically switch between object detection templates to match a determined object type, configuration, orientation, pose, blur, exposure and/or directional illumination condition.