Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.

Abstract: 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.