Qualcomm Incorporated

Rana Ali Amjad

Joseph Binamira Soriaga

Max Welling

Arash Behboodi

Taesang Yoo

Lorenzo Ferrari

Tijmen Pieter Frederik Blankevoort

Farhad Ghazvinian Zanjani

Markus Nagel

Marinus Willem Van Baalen

Christos Louizos

Kumar Pratik

Various embodiments include methods and devices for joint mixed-precision quantization and structured pruning. Embodiments may include determining whether a plurality of gates of quantization and pruning gates are selected for combination, and in response to determining that the plurality of gates are selected for combination, iteratively for each successive gate of the plurality of gates selected for combination quantizing a residual error of a quantized tensor to a scale of a next bit-width producing a residual error quantized tensor in which the next bit-width increases for each successive iteration, and adding the quantized tensor and the residual error quantized tensor producing a next quantized tensor in which the next quantized tensor has the next bit-width, and in which the next quantized tensor is the quantized tensor for a successive iteration.

Bayesian bits joint mixed-precision quantization and structured pruning using decomposed quantization and Bayesian gates

A method of training an artificial neural network (ANN), receives, from a base station, signal information for a radio frequency signal between the base station and a user equipment (UE). The artificial neural network is trained to determine a location of the UE and to map the environment based on the received signal information and in the absence of labeled data.

Unsupervised learning for simultaneous localization and mapping in deep neural networks using channel state information

A method performed by a communication device includes generating an initial channel estimate of a channel for a current time step with a Kalman filter based on a first signal received at the communication device. The method also includes inferring, with a neural network, a residual of the initial channel estimate of the current time step. The method further includes updating the initial channel estimate of the current time step based on the residual.

Neural network augmentation for wireless channel estimation and tracking

Growing community of inventors

Amsterdam, Netherlands

Rana Ali Amjad