Abstract: Examples to selectively generate a masked image include: a convolutional neural network detector to detect a first feature and a second feature in an image captured by a camera; a feature recognizer to determine the first feature is a displayable feature and the second feature is a non-displayable feature by comparing the first and second features of the image to reference feature images stored in a memory; and a blur generator to generate the masked image to display the displayable feature and mask the non-displayable feature.

Abstract: Methods, systems, articles of manufacture and apparatus to generate digital scenes are disclosed. An example apparatus to generate labelled models includes a map builder to generate a three-dimensional (3D) model of an input image, a grouping classifier to identify a first zone of the 3D model corresponding to a first type of grouping classification, a human model builder to generate a quantity of placeholder human models corresponding to the first zone, a coordinate engine to assign the quantity of placeholder human models to respective coordinate locations of the first zone, the respective coordinate locations assigned based on the first type of grouping classification, a model characteristics modifier to assign characteristics associated with an aspect type to respective ones of the quantity of placeholder human models, and an annotation manager to associate the assigned characteristics as label data for respective ones of the quantity of placeholder human models.

Abstract: A disclosed example to operate a mobile camera includes recognizing a first feature in first sensor data in response to the first feature being detected in the first sensor data; transitioning the mobile camera from a first feature detection state to a second feature detection state in response to the recognizing of the first feature, the mobile camera to operate using higher power consumption in second feature detection state than in the first feature detection state; recognizing a second feature in second sensor data in the second feature detection state; and sending to an external device at least one of first metadata corresponding to the first feature or second metadata corresponding to the second feature.

Abstract: The present application discloses a computing device that can provide a low-power, highly capable computing platform for computational imaging. The computing device can include one or more processing units, for example one or more vector processors and one or more hardware accelerators, an intelligent memory fabric, a peripheral device, and a power management module. The computing device can communicate with external devices, such as one or more image sensors, an accelerometer, a gyroscope, or any other suitable sensor devices.

Abstract: The present application provides a method of randomly accessing a compressed structure in memory without the need for retrieving and decompressing the entire compressed structure.

Abstract: A vector processor is disclosed including a variety of variable-length instructions. Computer-implemented methods are disclosed for efficiently carrying out a variety of operations in a time-conscious, memory-efficient, and power-efficient manner. Methods for more efficiently managing a buffer by controlling the threshold based on the length of delay line instructions are disclosed. Methods for disposing multi-type and multi-size operations in hardware are disclosed. Methods for condensing look-up tables are disclosed. Methods for in-line alteration of variables are disclosed.

Abstract: Systems and methods are provided for rendering of a dual eye-specific display. The system tracks the user's eye movements and/or positions, in some implementations, based on electroencephalography (EEG) of the user, to correctly label the central (foveal) and peripheral (extra-foveal) areas of the display. Foveal data is fully rendered while extra-foveal data is reduced in resolution and, in some implementations, shared between the two displays.

Abstract: Examples to selectively generate a masked image include: a convolutional neural network detector to detect a first feature and a second feature in an image captured by a camera; a feature recognizer to determine the first feature is a displayable feature and the second feature is a non-displayable feature by comparing the first and second features of the image to reference feature images stored in a memory; and a blur generator to generate the masked image to display the displayable feature and mask the non-displayable feature.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to improve convolution efficiency of a convolution neural network (CNN) accelerator. An example hardware accelerator includes a hardware data path element (DPE) in a DPE array, the hardware DPE including an accumulator, and a multiplier coupled to the accumulator, the multiplier to multiply first inputs including an activation value and a filter coefficient value to generate a first convolution output when the hardware DPE is in a convolution mode, and a controller coupled to the DPE array, the controller to adjust the hardware DPE from the convolution mode to a pooling mode by causing at least one of the multiplier or the accumulator to generate a second convolution output based on second inputs, the second inputs including an output location value of a pool area, at least one of the first inputs different from at least one of the second inputs.

Abstract: An example mobile camera includes a first convolutional neural network to recognize a first feature in first sensor data in response to the first feature being detected in the first sensor data; a state transitioner to transition the mobile camera from a first feature detection state to a second feature detection state in response to the first convolutional neural network recognizing the first feature, the mobile camera to operate using higher power consumption in the second feature detection state than in the first feature detection state; a second convolutional neural network to recognize a second feature in second sensor data in the second feature detection state; and a communications interface to send to an external device at least one of first metadata corresponding to the first feature or second metadata corresponding to the second feature.

Abstract: A vector processor is disclosed including a variety of variable-length instructions. Computer-implemented methods are disclosed for efficiently carrying out a variety of operations in a time-conscious, memory-efficient, and power-efficient manner. Methods for more efficiently managing a buffer by controlling the threshold based on the length of delay line instructions are disclosed. Methods for disposing multi-type and multi-size operations in hardware are disclosed. Methods for condensing look-up tables are disclosed. Methods for in-line alteration of variables are disclosed.

Abstract: Example methods, apparatus, systems and articles of manufacture (e.g., physical storage media) to implement video surveillance with neural networks are disclosed. Example systems disclosed herein include a database to store records of operator-labeled video segments (e.g., as records of operator-labeled video segments). The operator-labeled video segments include reference video segments and corresponding reference event labels describing the video segments. Disclosed example systems also include a neural network including a first instance of an inference engine, and a training engine to train the first instance of the inference engine based on a training set of the operator-labeled video segments obtained from the database, the first instance of the inference engine to infer events from the operator-labeled video segments included in the training set.

Abstract: The present application discloses a computing device that can provide a low-power, highly capable computing platform for computational imaging. The computing device can include one or more processing units, for example one or more vector processors and one or more hardware accelerators, an intelligent memory fabric, a peripheral device, and a power management module. The computing device can communicate with external devices, such as one or more image sensors, an accelerometer, a gyroscope, or any other suitable sensor devices.

Abstract: The present application relates generally to a parallel processing device. The parallel processing device can include a plurality of processing elements, a memory subsystem, and an interconnect system. The memory subsystem can include a plurality of memory slices, at least one of which is associated with one of the plurality of processing elements and comprises a plurality of random access memory (RAM) tiles, each tile having individual read and write ports. The interconnect system is configured to couple the plurality of processing elements and the memory subsystem. The interconnect system includes a local interconnect and a global interconnect.

Abstract: Systems and methods are provided for image classification using histograms of oriented gradients (HoG) in conjunction with a trainer. The efficiency of the process is greatly increased by first establishing a bitmap which identifies a subset of the pixels in the HoG window as including relevant foreground information, and limiting the HoG calculation and comparison process to only the pixels included in the bitmap.

Abstract: Systems and methods are provided for rendering of a dual eye-specific display. The system tracks the user's eye movements and/or positions, in some implementations, based on electroencephalography (EEG) of the user, to correctly label the central (foveal) and peripheral (extra-foveal) areas of the display. Foveal data is fully rendered while extra-foveal data is reduced in resolution and, in some implementations, shared between the two displays.

Abstract: An example includes sending first weight values to first client devices; accessing sets of updated weight values provided by the first client devices, the updated weight values generated by the first client devices training respective first convolutional neural networks (CNNs) based on: the first weight values, and sensor data generated at the client devices; testing performance in a second CNN of at least one of: the sets of the updated weight values, or a combination of ones of the updated weight values from the sets of the updated weight values; selecting server-synchronized weight (SSW) values from the at least one of: the sets of the updated weight values, or a combination of ones of the updated weight values from the sets of the updated weight values; and sending the SSW values to at least one of: at least some of the first client devices, or second client devices.

Abstract: The present application relates generally to a parallel processing device. The parallel processing device can include a plurality of processing elements, a memory subsystem, and an interconnect system. The memory subsystem can include a plurality of memory slices, at least one of which is associated with one of the plurality of processing elements and comprises a plurality of random access memory (RAM) tiles, each tile having individual read and write ports. The interconnect system is configured to couple the plurality of processing elements and the memory subsystem. The interconnect system includes a local interconnect and a global interconnect.

Abstract: An example mobile camera includes a first convolutional neural network to recognize a first feature in first sensor data in response to the first feature being detected in the first sensor data; a state transitioner to transition the mobile camera from a first feature detection state to a second feature detection state in response to the first convolutional neural network recognizing the first feature, the mobile camera to operate using higher power consumption in the second feature detection state than in the first feature detection state; a second convolutional neural network to recognize a second feature in second sensor data in the second feature detection state; and a communications interface to send to an external device at least one of first metadata corresponding to the first feature or second metadata corresponding to the second feature.

Abstract: Methods, systems, apparatus and articles of manufacture are disclosed herein to improve image classification with boundary-bitmaps. An example disclosed apparatus includes a silhouette engine to identify a foreground silhouette within the image, generate a bounding box based on borders of the foreground silhouette, and generate an encoded silhouette matrix which identifies cells of a foreground and cells of a background, a convolution cell selector to convolve the encoded silhouette matrix to generate a convoluted bitmap matrix, and a filter cell selector to improve image classification efficiency by identifying eligible blocks of the convoluted bitmap matrix by retaining first respective cells of the convoluted bitmap matrix that satisfy a cell retention threshold, and removing second respective cells of the convoluted bitmap matrix that do not satisfy the cell retention threshold.