Qualcomm Incorporated

Intel Corporation

Casimir Matthew Wierzynski

Bardia Fallah Behabadi

Aliakbar Aghamohammadi

Sarah Paige Gibson

Saurav Agarwal

Christopher G Lott

Kiran Kumar Somasundaram

Gerhard Reitmayr

Serafin Diaz Spindola

Shayegan Omidshafiei

David Jonathan Julian

Venkata Sreekanta Reddy Annapureddy

Dexu Lin

Michael Campos

Rosario Cammarota

Anthony Sarah

Wenhua Lin

Amir Khosrowshahi

Fabian Boemer

An example private processing pipeline may include: a masked decryption unit to perform a masked decryption operation transforming input data into masked decrypted data; a masked functional unit to produce a masked result by performing a masked operation on the masked decrypted data; and a masked encryption unit to perform a masked encryption operation transforming the masked result into an encrypted result.

Processor with private pipeline

Embodiments of the present disclosure are directed toward techniques and apparatus comprising at least one layer of an ONN that includes an optical matrix multiplier provided in a semiconductor substrate to receive a plurality of optical signal inputs and to linearly transform the plurality of optical signal inputs into a plurality of optical signal outputs. The optical matrix multiplier comprises one or more 2&#xd7;2 unitary optical matrices optically interconnected to implement a singular value decomposition (SVD) of a matrix, and a nonlinear optical device coupled with the optical matrix multiplier in the semiconductor substrate, to receive the optical signal outputs and to provide an optical output that is generated in a nonlinear manner in response to the optical signal outputs of the optical matrix multiplier reaching saturation or attenuation. Additional embodiments may be described and claimed.

Optical analog matrix multiplier for optical neural networks

A method of transfer learning includes receiving second data and generating, via a first network, second labels for the second data. In one configuration, the first network has been previously trained on first labels for first data. Additionally, the second labels are generated for training a second network.

Transfer learning in neural networks

A method of visual navigation for a robot includes integrating a depth map with localization information to generate a three-dimensional (3D) map. The method also includes motion planning based on the 3D map, the localization information, and/or a user input. The motion planning overrides the user input when a trajectory and/or a velocity, received via the user input, is predicted to cause a collision.

Autonomous visual navigation

A method for defining a sensor model includes determining a probability of obtaining a measurement from multiple potential causes in a field of view of a sensor modeled based on a stochastic map. The stochastic map includes a mean occupancy level for each voxel in the stochastic map and a variance of the mean occupancy level for each pixel. The method also includes determining a probability of obtaining an image based on the determined probability of obtaining the measurement. The method further includes planning an action for a robot, comprising the sensor, based on the probability of obtaining the image.

Stochastic map-aware stereo vision sensor model

A method for selecting bit widths for a fixed point machine learning model includes evaluating a sensitivity of model accuracy to bit widths at each computational stage of the model. The method also includes selecting a bit width for parameters, and/or intermediate calculations in the computational stages of the mode. The bit width for the parameters and the bit width for the intermediate calculations may be different. The selected bit width may be determined based on the sensitivity evaluation.

Bit width selection for fixed point neural networks

A method substantially simultaneously plans a path and maps an environment by a robot. The method determines a mean of an occupancy level for a location in a map. The method also includes determining a probability distribution function (PDF) of the occupancy level. The method further includes calculating a cost function based on the PDF. Finally, the method includes simultaneously planning the path and mapping the environment based on the cost function.

Simultaneous mapping and planning by a robot

An artificial neural network may be configured to test the impact of certain input parameters. To improve testing efficiency and to avoid test runs that may not alter system performance, the effect of input parameters on neurons or groups of neurons may be determined to classify the neurons into groups based on the impact of certain parameters on those groups. Groups may be ordered serially and/or in parallel based on the interconnected nature of the groups and whether the output of neurons in one group may affect the operation of another. Parameters not affecting group performance may be pruned as inputs to that particular group prior to running system tests, thereby conserving processing resources during testing.

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San Diego, CA, United States of America

Casimir Matthew Wierzynski