Abstract: Systems, computer program products, and methods are described for secure data transmission. An example system includes a first end-point device, an intermediate device, and a second-end point device. The first end-point device determines the format requirements of the communication link between the first end-point device and the intermediate device, and the communication link intermediate device and the second end-point device. Based on the format requirements, the first end-point device configures the data packet for transmission, such that the data packet, when received at the intermediate device, is re-configured and routed to the second end-point device. When the second end-point device receives the data packet, it verifies the data packet to confirm that the packet has maintained its integrity throughout transit.

Abstract: A method of compression includes compressing a captured image from a first bit-depth at which it was captured to a second bit-depth that is less than the first bit-depth. The compression comprises applying, according to a power curve, a first compression amount to a first region of the captured image having a first pixel value; and applying, according to the power curve, a second compression amount to a second part of the captured image having a higher second pixel value, wherein the first compression amount is lower than the second compression amount.

Abstract: Apparatuses, systems, and techniques to perform signal processing operations in a fifth generation (“5G”) radio signal. In at least one embodiment, one or more processors equalize, in parallel, one or more 5G radio signals.

Abstract: In various examples, live perception from sensors of a vehicle may be leveraged to generate object tracking paths for the vehicle to facilitate navigational controls in real-time or near real-time. For example, a deep neural network (DNN) may be trained to compute various outputs—such as feature descriptor maps including feature descriptor vectors corresponding to objects included in a sensor(s) field of view. The outputs may be decoded and/or otherwise post-processed to reconstruct object tracking and to determine proposed or potential paths for navigating the vehicle.

Abstract: In various examples, users may access a tool that automatically generates video montages from video clips of the user's gameplay according to parameterized recipes. As a result, a user may select—or allow the system to select—clips corresponding to gameplay of the user and customize one or more parameters (e.g., transitions, music, audio, graphics, etc.) of a recipe, and a video montage may be generated automatically according to a montage script output using the recipe. As such, a user may have a video montage generated with little user involvement, and without requiring any skill or expertise in video editing software. In addition, even for experienced video editors, automatic video montage generation may be a useful alternative to save the time and effort of manually curating video montages.

Abstract: Various embodiments include a parallel processing computer system that enables parallel instances of a program to synchronize at disparate addresses in memory. When the parallel program instances need to exchange data, the program instances synchronize based on a mask that identifies the program instances that are synchronizing. As each program instance reaches the point of synchronization, the program instance blocks and waits for all other program instances to reach the point of synchronization. When all program instances have reached the point of synchronization, at least one program instance executes a synchronous operation to exchange data. The program instances then continue execution at respective and disparate return addresses.

Abstract: Apparatuses, systems, and techniques to perform effective tone management for image data. In an embodiment, a set of contrast gain curves are generated corresponding to a set of tonal ranges of an input image. An output image may then be generated by at least applying corresponding contrast gain curves to tonal ranges of the input image.

Abstract: Approaches presented herein provide for semantic data matching, as may be useful for selecting data from a large unlabeled dataset to train a neural network. For an object detection use case, such a process can identify images within an unlabeled set even when an object of interest represents a relatively small portion of an image or there are many other objects in the image. A query image can be processed to extract image features or feature maps from only one or more regions of interest in that image, as may correspond to objects of interest. These features are compared with images in an unlabeled dataset, with similarity scores being calculated between the features of the region(s) of interest and individual images in the unlabeled set. One or more highest scored images can be selected as training images showing objects that are semantically similar to the object in the query image.

Abstract: Approaches in accordance with various illustrative embodiments provide for the management of active connections in a network environment. In particular, various embodiments implement keep alive functionality in components such as network processing units (NPUs) of network devices such as routers and switches, instead of host processors for those devices. When a status message is received, such as a hello message, the NPU can set or refresh a hit bit in a table entry for a given connection with a peer device. If a subsequent status message is not received within a keep alive interval of the last received status message, then the NPU can determine that the connection with the peer device is stale and can inform the host processor that the connection is no longer available for routing network traffic. The status messages are terminated in the NPU and prevented from being received and processed by the host processor.

Abstract: In various examples, motifs, watermarks, and/or signature inputs are applied to a deep neural network (DNN) to detect faults in underlying hardware and/or software executing the DNN. Information corresponding to the motifs, watermarks, and/or signatures may be compared to the outputs of the DNN generated using the motifs, watermarks and/or signatures. When a the accuracy of the predictions are below a threshold, or do not correspond to the expected predictions of the DNN, the hardware and/or software may be determined to have a fault—such as a transient, an intermittent, or a permanent fault. Where a fault is determined, portions of the system that rely on the computations of the DNN may be shut down, or redundant systems may be used in place of the primary system. Where no fault is determined, the computations of the DNN may be relied upon by the system.

Abstract: In various examples, an end-to-end perception evaluation system for autonomous and semi-autonomous machine applications may be implemented to evaluate how the accuracy or precision of outputs of machine learning models—such as deep neural networks (DNNs)—impact downstream performance of the machine when relied upon. For example, decisions computed by the system using ground truth output types may be compared to decisions computed by the system using the perception outputs. As a result, discrepancies in downstream decision making of the system between the ground truth information and the perception information may be evaluated to either aid in updating or retraining of the machine learning model or aid in generating more accurate or precise ground truth information.

Abstract: Embodiments of the present disclosure relate to parallel mask rule checking on evolving mask shapes in optical proximity correction (OPC) flows for integrated circuit designs. Systems and methods are disclosed that perform mask (manufacturing) rule checks (MRC) in parallel, sharing information to maintain symmetry when violations are corrected. In an embodiment the shared information is also used to minimize changes to the geometric area of proposed mask shapes resulting from the OPC. In contrast to conventional systems, MRC is performed for multiple edges in parallel, sharing information between the different edges to encourage symmetry. In an embodiment, all edges may be adjusted in parallel to reduce mask-edge traversal bias.

Abstract: In various examples, systems and methods are disclosed relating to generating tokens for traffic modeling. One or more circuits can identify trajectories in a dataset, and generate actions from the identified trajectories. The one or more circuits can generate, based at least on the plurality of actions and at least one trajectory of the plurality of trajectories, a set of tokens representing actions to generate trajectories of one or more agents in a simulation. The one or more circuits may update a transformer model to generate simulated actions for simulated agents based at least on tokens generated from the trajectories in the dataset.

Abstract: In various examples, potentially highlight-worthy video clips are identified from a gameplay session that a gamer might then selectively share or store for later viewing. The video clips may be identified in an unsupervised manner based on analyzing game data for durations of predicted interest. A classification model may be trained in an unsupervised manner to classify those video clips without requiring manual labeling of game-specific image or audio data. The gamer can select the video clips as highlights (e.g., to share on social media, store in a highlight reel, etc.). The classification model may be updated and improved based on new video clips, such as by creating new video-clip classes.

Abstract: Apparatuses, systems, and techniques to identify orientations of objects within images. In at least one embodiment, one or more neural networks are trained to identify an orientations of one or more objects based, at least in part, on one or more characteristics of the object other than the object's orientation.

Abstract: Technologies for optimizing post-FEC bit error rate performance of a Forward Error Correction (FEC) system are described. A controller is coupled to an FEC circuit and a receiver circuit. The controller receives FEC symbol error data from the FEC circuit and determines, using the FEC symbol error data, a post-FEC correlated performance metric indicative of an estimated post-FEC BER of the FEC circuit. The controller adjusts, based on the post-FEC correlated performance metric, at least one of a FEC parameter of the FEC circuit or a link parameter of the receiver circuit to decrease the estimated post-FEC BER. This improves the post-FEC BER performance of the FEC circuit.

Abstract: In various examples, sensor data representative of an image of a field of view of a vehicle sensor may be received and the sensor data may be applied to a machine learning model. The machine learning model may compute a segmentation mask representative of portions of the image corresponding to lane markings of the driving surface of the vehicle. Analysis of the segmentation mask may be performed to determine lane marking types, and lane boundaries may be generated by performing curve fitting on the lane markings corresponding to each of the lane marking types. The data representative of the lane boundaries may then be sent to a component of the vehicle for use in navigating the vehicle through the driving surface.

Abstract: Circuitry and a method of operating a clock monitoring circuit for monitoring a clock signal is disclosed. The method comprises charging a first capacitor connected to a connection between a first pair of transistors and a voltage reference, charging a second capacitor connected to a connection between a second pair of transistors and to the voltage reference, sinking a current from the first and second pair of transistors with a constant current sink, and asserting a clock slow detect (CSD) signal when a voltage at the constant current sink drops below a threshold indicating durations of phases of the clock signal lengthen.

Abstract: Systems and methods are directed toward evaluating auditory inputs against a range of tolerance to provide feedback regarding pronunciation. An auditory input may be evaluated using a trained machine learning system and evaluated for similarity against a target word. Similarity may be scored and then evaluated to determine whether the similarity falls within a range of tolerance, wherein the range of tolerance may be adjusted or modified for particular uses. A score within the range of tolerance is indicative of a word that has been pronounced such that it would be perceptible.

Abstract: One or more embodiments relate to executing a software testing tool to identify function calls—internal and/or external—of software code and their corresponding errors. Once identified-such as during an information gathering operation-the error codes may be returned in place of actual outputs of the function during testing, and the downstream processing of the software as a result of the errors may be evaluated. As such, an automatic software testing tool may be implemented that not only identifies functions calls and corresponding errors, but also evaluates performance of the software in view of the various different error types associated with the function calls.