Abstract:
Improved face tracking is provided during determination of an image by an imaging device using a low power face tracking unit. In one embodiment, image data associated with a frame and one or more face detection windows from a face detection unit may be received by the face tracking unit. The face detection windows are associated with the image data of the frame. A face list may be determined based on the face detection windows and one or more faces may be selected from the face list to generate an output face list. The output face list may then be provided to a processor of an imaging device for the detection of an image based on at least one of coordinate and scale values of the one or more faces on the output face list.
Abstract:
A cache coherency controller, a system comprising such, and a method of its operation are disclosed. The coherency controller ensures that target-side security checking rules are not violated by the performance-improving processes commonly used in coherency controllers such as dropping, merging, invalidating, forwarding, and snooping. This is done by ensuring that requests marked for target-side security checking and any other requests to overlapping addresses are forwarded directly to the target-side security filter without modification or side effects.
Abstract:
A method for classifying a human-object interaction includes identifying a human-object interaction in the input. Context features of the input are identified. Each identified context feature is compared with the identified human-object interaction. An importance of the identified context feature is determined for the identified human-object interaction. The context feature is fused with the identified human-object interaction when the importance is greater than a threshold.
Abstract:
Certain aspects of the present disclosure provide techniques and apparatus for improved program synthesis using machine learning. An input indicating a programming task is accessed. A generated program is generated based on processing the input using a trained machine learning model. In response to determining that the generated program failed to satisfy the programming task, feedback is generated, and a revised program is generated based on processing the feedback using the trained machine learning model. In response to determining that the revised program satisfied the programming task, one or more parameters of the trained machine learning model are updated based on the revised program.
Abstract:
Systems, devices, methods, and implementations related to contact detection are described herein. In one aspect, a system is provided. The system includes a first piezoelectric microelectromechanical systems (MEMS) transducer coupled to configured to generate a first analog signal when the first analog signal is transduced from vibrations propagating through the object. The system includes a second piezoelectric MEMS transducer having configured to generate a second analog signal transduced from acoustic vibrations at a location of the object, and classification circuitry coupled to the output of first piezoelectric MEMS transducer and the output of the second piezoelectric MEMS transducer, where the classification circuitry is configured to process data from the first analog signal and data from the second analog signal, and to categorize combinations of the first analog signal and the second analog signal received during one or more time frames.
Abstract:
The present disclosure relates to methods and apparatus for graphics processing. The apparatus may identify at least one mesh associated with at least one frame. The apparatus may also divide the at least one mesh into a plurality of groups of primitives, each of the plurality of groups of primitives including at least one primitive and a plurality of vertices. The apparatus may also compress the plurality of groups of primitives into a plurality of groups of compressed primitives, the plurality of groups of compressed primitives being associated with random access. Additionally, the apparatus may decompress the plurality of groups of compressed primitives, at least one first group of the plurality of groups of compressed primitives being decompressed in parallel with at least one second group of the plurality of groups of compressed primitives.
Abstract:
Embodiments include methods, and processing devices for implementing the methods. Various embodiments may include calculating a batch softmax normalization factor using a plurality of logit values from a plurality of logits of a layer of a neural network, normalizing the plurality of logit values using the batch softmax normalization factor, and mapping each of the normalized plurality of logit values to one of a plurality of manifolds in a coordinate space. In some embodiments, each of the plurality of manifolds represents a number of labels to which a logit can be classified. In some embodiments, at least one of the plurality of manifolds represents a number of labels other than one label.
Abstract:
Certain aspects of the present disclosure provide techniques and apparatuses for inferencing against a multidimensional point cloud using a machine learning model. An example method generally includes generating a score for each respective point in a multidimensional point cloud using a scoring neural network. Points in the multidimensional point cloud are ranked based on the generated score for each respective point in the multidimensional point cloud. The top points are selected from the ranked multidimensional point cloud, and one or more actions are taken based on the selected top k points.
Abstract:
An apparatus for testing a transducer module includes a test signal generator coupled to a common-mode terminal common to a plurality of transducers, and a signal processing circuit configured to receive output signal from each of said transducers and to produce an output signal. If the transducers are well matched to one another, the output signal will have little or no output amplitude. If there is a mismatch between the transducers, however, the output signal will have an amplitude proportional to the mismatch. The amplitude of the output signal may be compared to a predetermined threshold in order to produce a mismatch output signal indicating the existence of, and/or the degree of, mismatch between the transducers.
Abstract:
A transducer comprising: at least one piezoelectric layer; a first patterned conductive layer that is patterned with a first opening; a second patterned conductive layer that is patterned with a second opening; wherein at least one piezoelectric layer is between the first and the second patterned conductive layers in a stack; and wherein a position of the first opening is staggered relative to a position of the second opening in the stack to mitigate an occurrence of crack propagation through the layers.