Abstract: Techniques are provided for generation of synthetic 3-dimensional object image variations for training of recognition systems. An example system may include an image synthesizing circuit configured to synthesize a 3D image of the object (including color and depth image pairs) based on a 3D model. The system may also include a background scene generator circuit configured to generate a background for each of the rendered image variations. The system may further include an image pose adjustment circuit configured to adjust the orientation and translation of the object for each of the variations. The system may further include an illumination and visual effect adjustment circuit configured to adjust illumination of the object and the background for each of the variations, and to further adjust visual effects of the object and the background for each of the variations based on application of simulated camera parameters.

Abstract: In an example, an apparatus comprises a plurality of execution units; and logic, at least partially including hardware logic, to determine a sub-graph of a network that can be executed in a frequency domain and apply computations in the sub-graph in the frequency domain. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises logic, at least partially including hardware logic, to save one or more outputs of a deep learning neural network in a storage system of an autonomous vehicle and upload the one or more outputs to a remote server. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of execution units and logic, at least partially including hardware logic, to gate at least one of a multiply unit or an accumulate unit in response to an input of value zero. Other embodiments are also disclosed and claimed.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed that optimize layers of a machine learning model for a target hardware platform. An example apparatus includes a communication processor to obtain information specific to the target hardware platform (THP) on which to execute the machine learning model; a layer generation controller to generate layers of the machine learning model based on the information specific to the THP; and a deployment controller to, in response to the machine learning model satisfying a threshold error metric, deploy the machine learning model to the THP.

Abstract: In an example, an apparatus comprises a plurality of execution units and logic, at least partially including hardware logic, to gate at least one of a multiply unit or an accumulate unit in response to an input of value zero. Other embodiments are also disclosed and claimed.

Abstract: Techniques are provided for generation of synthetic 3-dimensional object image variations for training of recognition systems. An example system may include an image synthesizing circuit configured to synthesize a 3D image of the object (including color and depth image pairs) based on a 3D model. The system may also include a background scene generator circuit configured to generate a background for each of the rendered image variations. The system may further include an image pose adjustment circuit configured to adjust the orientation and translation of the object for each of the variations. The system may further include an illumination and visual effect adjustment circuit configured to adjust illumination of the object and the background for each of the variations, and to further adjust visual effects of the object and the background for each of the variations based on application of simulated camera parameters.

Abstract: In an example, an apparatus comprises a plurality of execution units; and logic, at least partially including hardware logic, to determine a sub-graph of a network that can be executed in a frequency domain and apply computations in the sub-graph in the frequency domain. Other embodiments are also disclosed and claimed.

Abstract: A system and method for close range object tracking are described. Close range depth images of a user's hands and fingers or other objects are acquired using a depth sensor. Using depth image data obtained from the depth sensor, movements of the user's hands and fingers or other objects are identified and tracked, thus permitting the user to interact with an object displayed on a screen, by using the positions and movements of his hands and fingers or other objects.

Abstract: An activity recording system is provided. The activity recording system includes a three-dimensional camera, a sensor arrangement that is fitted to a subject being recorded, and an activity recording device. The activity recording device receives image information from the three-dimensional camera and sensor arrangement information from the sensor arrangement. Both the image information and the sensor arrangement information include location measurements. The sensor arrangement information is generated by location sensors that are positioned at target features of the subject to be tracked. The sensor arrangement information is a key to the image information that specifies where, in any given image, the target features of the subject lie. Activity data having these characteristics may be applied to solve a variety of system development problems. Such activity data can be used to training machine learning components or test computer vision components for a fraction of the cost of using conventional techniques.

Abstract: In an example, an apparatus comprises a plurality of execution units; and logic, at least partially including hardware logic, to determine a sub-graph of a network that can be executed in a frequency domain and apply computations in the sub-graph in the frequency domain. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of execution units and logic, at least partially including hardware logic, to gate at least one of a multiply unit or an accumulate unit in response to an input of value zero. Other embodiments are also disclosed and claimed.

Abstract: An activity recording system is provided. The activity recording system includes a three-dimensional camera, a sensor arrangement that is fitted to a subject being recorded, and an activity recording device. The activity recording device receives image information from the three-dimensional camera and sensor arrangement information from the sensor arrangement. Both the image information and the sensor arrangement information include location measurements. The sensor arrangement information is generated by location sensors that are positioned at target features of the subject to be tracked. The sensor arrangement information is a key to the image information that specifies where, in any given image, the target features of the subject lie. Activity data having these characteristics may be applied to solve a variety of system development problems. Such activity data can be used to training machine learning components or test computer vision components for a fraction of the cost of using conventional techniques.

Abstract: Apparatus, devices, systems, methods, and articles of manufacture are disclosed to allocate power in a computing device. An example system includes a compiler to: analyze power consumption behavior of power consumption units of the computing device; build a power profile; and generate source code with hints of the power profile. The example system includes a power control circuit to: develop a power policy using the hints of the power profile and requests for power licenses from the power consumption units of the computing device; and allocate power to the power consumption units based on the power profile.

Abstract: Methods, apparatus, systems and articles of manufacture are disclosed to map workloads. An example apparatus includes a constraint definer to define performance characteristic targets of the neural network, an action determiner to apply a first resource configuration to candidate resources corresponding to the neural network, a reward determiner to calculate a results metric based on (a) resource performance metrics and (b) the performance characteristic targets, and a layer map generator to generate a resource mapping file, the mapping file including respective resource assignments for respective corresponding layers of the neural network, the resource assignments selected based on the results metric.

Abstract: In an example, an apparatus comprises a plurality of execution units and logic, at least partially including hardware logic, to gate at least one of a multiply unit or an accumulate unit in response to an input of value zero. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of execution units; and logic, at least partially including hardware logic, to determine a sub-graph of a network that can be executed in a frequency domain and apply computations in the sub-graph in the frequency domain. Other embodiments are also disclosed and claimed.

Abstract: A system and method for close range object tracking are described. Close range depth images of a user's hands and fingers or other objects are acquired using a depth sensor. Using depth image data obtained from the depth sensor, movements of the user's hands and fingers or other objects are identified and tracked, thus permitting the user to interact with an object displayed on a screen, by using the positions and movements of his hands and fingers or other objects.

Abstract: The disclosure provides a natural language processing (NLP) model arranged to operate on two lexicons, where one lexicon is a sub-set of the other lexicon. The NLP model can be arranged to generate output based on the sub-set lexicon and exit processing of the NLP model, to potentially save computation cycles.

Abstract: In an example, an apparatus comprises a plurality of execution units and logic, at least partially including hardware logic, to gate at least one of a multiply unit or an accumulate unit in response to an input of value zero. Other embodiments are also disclosed and claimed.