Abstract:
An adaptive active noise cancellation apparatus performs a filtering operation in a first digital domain and performs adaptation of the filtering operation in a second digital domain.
Abstract:
One aspect of the subject matter described may be implemented in a system for use in obtaining a deconvolved image of an object. In some implementations, the system may include an ultrasonic sensing system configured to perform an ultrasonic image scanning operation including one or more image scans of an object to obtain at least one measured image of the object. The system may include a processing unit configured to determine an initial estimate of a point spread function (PSF) associated with the ultrasonic image scanning operation based on the measured image. The processing unit may be configured to determine an initial estimate of a deconvolved image of the object based on the initial estimate of the PSF. The processing unit may be further configured to determine a refined estimate of the deconvolved image using an iterative deconvolution operation based on the initial estimates of the PSF and the deconvolved image.
Abstract:
An adaptive active noise cancellation apparatus performs a filtering operation in a first digital domain and performs adaptation of the filtering operation in a second digital domain.
Abstract:
Certain aspects of the present disclosure provide a method, including: storing a depthwise convolution kernel in a first one or more columns of a CIM array; storing a fused convolution kernel in a second one or more columns of the CIM array; storing pre-activations in one or more input data buffers associated with a plurality of rows of the CIM array; processing the pre-activations with the depthwise convolution kernel in order to generate depthwise output; modifying one or more of the pre-activations based on the depthwise output to generate modified pre-activations; and processing the modified pre-activations with the fused convolution kernel to generate fused output.
Abstract:
A method includes detecting a signal at a first set of receivers of a plurality of receivers of a device. The plurality of receivers includes the first set of receivers and a second set of receivers. The first set of receivers corresponds to selected receivers and the second set of receivers corresponds to non-selected receivers. The method includes predicting, based on the signal, an expected blockage of a signal path between a source of the signal and a first selected receiver of the first set of receivers, and selecting a particular receiver of the second set of receivers as a newly selected receiver in response to predicting the expected blockage.
Abstract:
A method includes receiving a first output from a first sensor of an electronic device and receiving a second output from a second sensor of the electronic device. The first sensor has a first sensor type and the second sensor has a second sensor type that is different from the first sensor type. The method also includes detecting a gesture based on the first output and the second output according to a complementary voting scheme that is at least partially based on gesture complexity.
Abstract:
An acoustic tracking system for determining a position of an object is provided that includes one or more receivers that detect an object based on an acoustic signal transmitted by one or more transmitters. The system also includes a processing component that determines a relative position of the detected object with respect to the one or more receivers and the one or more transmitters and selects at least three pairs of receivers and transmitters. Each selected pair includes a receiver from the one or more receivers and a transmitter from the one or more transmitters. The processing component also determines a position of the detected object using the selected at least three pairs of receivers and transmitters.
Abstract:
Certain aspects of the present disclosure provide techniques for performing machine learning computations in a compute in memory (CIM) array comprising a plurality of bit cells, including: determining that a sparsity of input data to a machine learning model exceeds an input data sparsity threshold; disabling one or more bit cells in the CIM array based on the sparsity of the input data prior to processing the input data; processing the input data with bit cells not disabled in the CIM array to generate an output value; applying a compensation to the output value based on the sparsity to generate a compensated output value; and outputting the compensated output value.
Abstract:
A compute-in-memory bitcell is provided that includes a pair of cross-coupled inverters for storing a stored bit. The compute-in-memory bitcell includes a logic gate formed by a pair of switches for multiplying the stored bit with an input vector bit. A controller controls the pair of switches responsive to a sign bit during a computation phase of operation and controls the pair of switches responsive to a magnitude bit during an execution phase of operation.
Abstract:
An adaptive active noise cancellation apparatus performs a filtering operation in a first digital domain and performs adaptation of the filtering operation in a second digital domain.