Abstract: An example apparatus having a heterogenous memory system includes a first sensor layer, of a plurality of stacked sensor layers, including an array of pixels; and one or more semiconductor layers of the plurality of stacked sensor layers located beneath the first sensor layer, the one or more semiconductor layers configured to process pixel data output by the array of pixels, the one or more semiconductor layers including a first memory to store most significant bits (“MSBs”) of data involved in the processing of the pixel data; a second memory to store least significant bits (“LSBs”) of the data; and wherein the first memory has a lower bit error rate (“BER”) than the second memory.

Abstract: A console and headset system locally trains machine learning models to perform customized online learning tasks. To customize the online learning models for specific users of the system without using outside resources, the system trains the models to compare a target frame to stored calibration frames, rather than directly inferring information about a target frame. During deployment, an embedding is generated for the target frame. A sample embedding that is closest to the target embedding is selected from a group of embeddings of calibration frames. The information about the selected embedding and target embedding and ground truths for the calibration frame are provided as inputs to one of the trained models. The model predicts a difference between the target frame and the calibration frame, which can be used to determine information about the target frame.

Abstract: The disclosed computer-implemented method may include (i) conditionally operating, at a first frequency, a first stage of an eye-tracking system processing pipeline that detects a region of interest and (ii) operating, at a second frequency that is substantially greater than the first frequency, a second stage of the eye-tracking system processing pipeline that predicts a gaze orientation based at least in part on the detected region of interest. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: The disclosed system may include a first layer that includes multiple digital pixel sensors configured to detect light. The system may also include a second layer that includes various image processing components configured to process the light detected by the digital pixel sensors. Still further, the system may include a third layer that includes machine learning (ML) hardware processing components. The image processing components of the second layer may be communicatively connected to the ML hardware processing components of the third layer via multiple micro through-silicon vias (uTSVs). Various other methods of manufacturing, apparatuses, and computer-readable media are also disclosed.

Abstract: In one example, an apparatus comprises: a first sensor layer, including an array of pixel cells configured to generate pixel data; and one or more semiconductor layers located beneath the first sensor layer with the one or more semiconductor layers being electrically connected to the first sensor layer via interconnects. The one or more semiconductor layers comprises on-chip compute circuits configured to receive the pixel data via the interconnects and process the pixel data, the on-chip compute circuits comprising: a machine learning (ML) model accelerator configured to implement a convolutional neural network (CNN) model to process the pixel data; a first memory to store coefficients of the CNN model and instruction codes; a second memory to store the pixel data of a frame; and a controller configured to execute the codes to control operations of the ML model accelerator, the first memory, and the second memory.

Abstract: A stacked camera-image-sensor circuit may include (i) a first layer that includes a plurality of image sensing elements, (ii) a second layer that includes components that interface with the image sensing elements, and (iii) at least one additional layer that includes image-processing components. Various other methods, systems, and computer-readable media are also disclosed.

Abstract: A sensor assembly for determining one or more features of a local area is presented herein. The sensor assembly includes a plurality of stacked sensor layers. A first sensor layer of the plurality of stacked sensor layers located on top of the sensor assembly includes an array of pixels. The top sensor layer can be configured to capture one or more images of light reflected from one or more objects in the local area. The sensor assembly further includes one or more sensor layers located beneath the top sensor layer. The one or more sensor layers can be configured to process data related to the captured one or more images. Different sensor architectures featuring various arrangements of memory and computing devices are described, some of which feature in-memory computing. A plurality of sensor assemblies can be integrated into an artificial reality system, e.g., a head-mounted display.