Qualcomm Incorporated

Ryan Michael Carey

Victor Hokkiu Chan

Raghu Narayan Challa

Roberto Padovani

Young Cheul Yoon

Mihir Vijay Laghate

Ashwin Deepak Swaminathan

Lorenzo Ferrari

Shankar Sadasivam

Vitor Rocha De Carvalho

Tadeusz Jarosinski

Abhijeet Bisain

Rajeev Jain

Lindsey Makana Kostas

Yuan Gao

Bhuwan Dhingra

Muralidhar Reddy Akula

Jinwon Lee

Aspects of the present disclosure relate to wireless communication systems, and in particular, to techniques for generation and processing of an embedding representing a beam for communication. Certain aspects provide a method for wireless communication by a wireless node. The method generally includes receiving an embedding representing a characterization associated with a beam; providing the embedding to a machine learning (ML) model; generating one or more communication parameters for communication using the beam via the ML model based on the embedding; and communicating using the one or more communication parameters.

Codebook embedding generation and processing

Methods and apparatus are provided for identifying environmental stimuli in an artificial nervous system using both spiking onset and spike counting. One example method of operating an artificial nervous system generally includes receiving a stimulus; generating, at an artificial neuron, a spike train of two or more spikes based at least in part on the stimulus; identifying the stimulus based at least in part on an onset of the spike train; and checking the identified stimulus based at least in part on a rate of the spikes in the spike train. In this manner, certain aspects of the present disclosure may respond with short response latencies and may also maintain accuracy by allowing for error correction.

Simultaneous latency and rate coding for automatic error correction

Certain aspects of the present disclosure support a method and apparatus for analog signal reconstruction and recognition via sub-threshold modulation. The analog waveform recognition in a sub-threshold region of an artificial neuron of the artificial nervous system can be performed by providing a predicted waveform in parallel to an input associated with the artificial neuron. The predicted waveform can be compared with the input and the signal can be generated based at least in part on the comparison. The signal can be a detection signal that detects matching and mismatching between the input and the predicted waveform

Analog signal reconstruction and recognition via sub-threshold modulation

A context aware system, for use in a mobile device, includes a context change detector (CCD) coupled to a context classifier (CCL). The CCD is configured to receive sensor data and to detect a change in a current context state of the mobile device based on the received sensor data. The CCL is configured to transition from a low power consumption mode to a normal power consumption mode in response to the CCD detecting the change in the current context state. The CCL is further configured to determine a next context state of the mobile device while in the normal power consumption mode.

Context aware system with multiple power consumption modes

Methods and apparatus for piecewise linear neuron modeling and implementing artificial neurons in an artificial nervous system based on linearized neuron models. One example method for operating an artificial neuron generally includes determining that a first state of the artificial neuron is within a first region; determining a second state of the artificial neuron based at least in part on a first set of linear equations, wherein the first set of linear equations is based at least in part on a first set of parameters corresponding to the first region; determining that the second state of the artificial neuron is within a second region; and determining a third state of the artificial neuron based at least in part on a second set of linear equations, wherein the second set of linear equations is based at least in part on a second set of parameters corresponding to the second region.

Piecewise linear neuron modeling

Growing community of inventors

San Diego, CA, United States of America

Ryan Michael Carey