Cornell University

Google Inc.

Xilinx, Inc.

William Marsh Rice University

Commissariat a L'energie Atomique Et Aux Energies Alternatives

Christoph Emanuel Studer

Christopher H Dick

Michael Wu

Richard G Baraniuk

Ilya Firman

Ficus Kirkpatrick

Jasper S Lin

Christian Sonntag

Linda K Nguyen

Michael Siliski

Andrew Devron Stadler

Aurash Jonathan Mahbod

Jonathan Brunsman

Aswin C Sankaranarayanan

Jianing Shi

Oscar Fernando Castañeda Fernandez

Olav Tirkkonen

James Brooks Miller

Daniel Marc Gatan Shiplacoff

Michael Adam Cohen

Selim Flavio Cinek

Joseph R Cavallaro

Lee Brandon Keely

Michael Pelissier

Michaël Pelissier

Thomas Ramon Karlo

Andrew E Waters

Bei Yin

Jorim Dorian Jaggi

Adrian Roos

Ramina Ghods

Seyed Hadi Mirfarshbafan

Alexandra Gallyas Sanhueza

Charles Jeon

Andrew S Lan

Aida Vosoughi

A parallel processor in associative content-addressable memory (PPAC) is provided. Processing in memory (PIM) moves computation into memories with the goal of improving throughput and energy-efficiency compared to traditional von Neumann-based architectures. Most existing PIM architectures are either general-purpose but only support atomistic operations, or are specialized to accelerate a single task. The PPAC described herein provides a novel in-memory accelerator that supports a range of matrix-vector-product (MVP)-like operations that find use in traditional and emerging applications. PPAC is, for example, able to accelerate low-precision neural networks, exact/approximate hash lookups, cryptography, and forward error correction. The fully-digital nature of PPAC enables its implementation with standard-cell-based complementary metal-oxide-semiconductor (CMOS), which facilitates automated design and portability among technology nodes. A comparison with recent digital and mixed-signal PIM accelerators reveals that PPAC is competitive in terms of throughput and energy-efficiency, while accelerating a wide range of applications and simplifying development.

Parallel processor in associative content addressable memory

Finite-alphabet beamforming for multi-antenna wideband systems is provided. The combination of massive multi-user multiple-input multiple-output (MU-MIMO) technology and millimeter-wave (mmWave) communication enables unprecedentedly high data rates for radio frequency (RF) communications. In such systems, beamforming must be performed at extremely high rates over hundreds of antennas. For example, spatial equalization applies beamforming in the uplink to mitigate interference among user equipment (UEs) at a base station (BS). Finite-alphabet equalization provides a new paradigm that restricts the entries of a spatial equalization matrix to low-resolution numbers, enabling high-throughput, low-power, and low-cost equalization hardware. Similarly, precoding applies beamforming in the downlink to maximize the reception of a signal transmitted from a BS to a target UE. Finite-alphabet precoding can be applied in the downlink to similarly improve power and cost in precoding hardware.

Finite-alphabet beamforming for multi-antenna wideband systems

A processing platform in illustrative embodiments comprises one or more processing devices each including at least one processor coupled to a memory. The processing platform is configured to extract channel features of a wireless channel of a wireless system from channel state information characterizing a radio geometry of the wireless channel, to generate a forward charting function that maps the extracted channel features to a channel chart characterizing a representational spatial geometry of the wireless channel, and to utilize the channel chart to estimate at least one position-related characteristic of one or more wireless devices in an actual spatial geometry of the wireless channel. Generating the forward charting function illustratively comprises performing an unsupervised learning process to learn the forward charting function from the extracted channel features. The channel chart is illustratively configured to preserve local geometry of multiple spatial locations associated with the extracted features in the actual spatial geometry of the wireless channel.

Channel charting in wireless systems

A wireless communication apparatus is provided. The wireless communication apparatus includes a denoising circuit configured to receive a noisy complex channel vector(s) in a spatial domain and convert the noisy complex channel vector(s) into a noisy beamspace-domain vector(s) in a beamspace domain. The denoising circuit determines an optimal denoising parameter and denoises the noisy beamspace-domain vector(s) based on the optimal denoising parameter to generate a denoised beamspace-domain vector(s). The denoising circuit then converts the denoised beamspace-domain vector(s) to a denoised complex channel vector(s) in the spatial domain. In examples discussed herein, the denoising circuit determines the optimal denoising parameter and denoises noisy beamspace-domain vector(s) based on a lower-complexity denoising algorithm having reduced computational complexity compared to existing denoising methods, thus helping to enable more accurate channel estimation in the wireless communication apparatus with reduced cost, footprint, and/or power consumption.

Circuit and method for enabling channel denoising in a wireless communication apparatus

A compressive sensing system for dynamic video acquisition. The system includes a video signal interface including a compressive imager configured to acquire compressive sensed video frame data from an object, a video processing unit including a processor and memory. The video processing unit is configured to receive the compressive sensed video frame data from the video signal interface. The memory comprises computer readable instructions that when executed by the processor cause the processor to generate a motion estimate from the compressive sensed video frame data and generate dynamical video frame data from the motion estimate and the compressive sensed video frame data. The dynamical video frame data may be output.

Methods and systems for video compressive sensing for dynamic imaging

A compressed sensing method based on non-uniform wavelet bandpass sampling. A K-sparse signal of interest is projected onto a sequence of waveforms succeeding one another at the bandpass sampling rate, the waveforms belonging to an overcomplete dictionary, the parameters of the waveforms depending on the characteristics of the bands of the signal. The correlation values are then non-uniformly sampled to provide a compressed representation of the signal.

Method of non-uniform wavelet bandpass sampling

A system includes an integrated circuit configured to communicating data in a channel. A channel matrix for the channel including a plurality of columns is received. A preprocessing step is performed, using a preprocessing unit, to compute a plurality of preprocessed column values corresponding to respective columns. An update step is performed, using an update unit, to update an estimation vector using a plurality of outer-loop iterations of an outer loop. Each outer-loop iteration updates the estimation vector using the plurality of preprocessed column values. An access link process is performed using the estimation vector.

Coordinate descent detector and precoder for multiple-input multiple-output (MIMO) system

An integrated circuit (IC) includes a downlink unit including an input to receive a first plurality of frequency domain (FD) symbols associated with data symbols for a plurality of users, and an iteration unit to perform a plurality of iterations based on adjustment values. Each iteration includes generating a second plurality of FD symbols by performing a precoding process based on the first plurality of FD symbols, generating a third plurality of time domain (TD) symbols by performing a first modulation process based on the second plurality of FD symbols, generating a fourth plurality of TD symbols by performing a dynamic range reduction process based on absolute values of the third plurality of TD symbols, and updating the adjustment values. The downlink unit further includes a decision unit configured to generate transmit TD symbols for transmission through a channel to the plurality of users.

System and method for downlink processing in communication systems

A mechanism for discerning user preferences for categories of provided content. A computer receives response data including a set of preference values that have been assigned to content items by content users. Output data is computed based on the response data using a latent factor model. The output data includes at least: an association matrix that defines K concepts associated with the content items, wherein K is smaller than the number of the content items, wherein, for each of the K concepts, the association matrix defines the concept by specifying strengths of association between the concept and the content items; and a concept-preference matrix including, for each content user and each of the K concepts, an extent to which the content user prefers the concept. The computer may display a visual representation of the association strengths in the association matrix and/or the extents in the concept-preference matrix.

Sparse factor analysis for analysis of user content preferences

A system includes a memory and an integrated circuit coupled to the memory. The integrated circuit is configured to communicate data in a channel characterized as a space having at least a frequency dimension. Anchor locations within the space correspond to respective regions of the space. The integrated circuit is further configured to determine a first inverse of a first matrix that corresponds to a first channel matrix for a first anchor location of the anchor locations. The first anchor location corresponds to a first region of the regions. The integrated circuit is further configured to perform an access link process for a second location other than the first anchor location but within the first region, the access link process using the first inverse determined for the first anchor location.

Adaptive multiple-input multiple-output (MIMO) data detection and precoding

A method for updating an application on a mobile device includes accessing, at the mobile device, application management information. The application management information identifies a plurality of applications installed on the mobile device and one or more granted permission settings associated with each installed application. The method also includes sending, from the mobile device to a server, a request for update information regarding each installed application and receiving, at the mobile device from the server, the update information. The update information identifies an update package for a respective application of the plurality of installed applications. The method also includes installing the update package without user interaction from a user of the mobile device if one or more required permission settings associated with the update package are included in the one or more granted permission settings associated with the respective application of the plurality of installed applications.

Automatic application updates

While a computing device is in a limited-access state, the computing device may output for display a user interface element. Responsive to the computing device receiving an indication of a first user input at a region of a presence-sensitive input device that corresponds to at least a portion of the user interface element as displayed, the computing device may alter the visual appearance of the user interface element to indicate that the user interface element is selected. Responsive to the computing device determining that it has received the indication of the second user input at the region of the presence-sensitive input device within the predefined period of time subsequent to receiving the indication of the first user input, the computing device may activate the user interface element.

Preventing unintentional user activation of user interface elements

An apparatus relating generally to matrix inversion is disclosed. This apparatus includes a matrix inversion module coupled to receive matrix information and to provide an approximation of an inversion of the matrix information. The matrix inversion module comprises a decomposition block coupled to receive the matrix information and to decompose the matrix information into diagonal matrix information and off diagonal matrix information, and an expansion block. The expansion block is coupled to receive the diagonal matrix information and the off diagonal matrix information, and to invert a matrix sum of the diagonal matrix information and the off diagonal matrix information by generation of a portion of a series expansion.

Matrix inversion

Growing community of inventors

Ithaca, NY, United States of America

Christoph Emanuel Studer