Abstract: An example method comprises receiving data points, determining at least one size of a plurality of subsets based on a constraint of at least one computation device or an analysis server, transferring each of the subsets to different computation devices, each computation device selecting a group of data points to generate a first sub-subset of landmarks, add non-landmark data points that have the farthest distance to the closest landmark to create an expanded sub-subset of landmarks, create an analysis landmark set based on a combination of expanded sub-subsets of expanded landmarks from different computation devices, perform a similarity function on the analysis landmark set, generate a cover of the mathematical reference space to create overlapping subsets, cluster the mapped landmark points based on the overlapping subsets, create a plurality of nodes, each node being based on the clustering, each landmark point being a member of at least one node.

Abstract: An adaptive generic receive offload (A-GRO) system and method are disclosed. In some embodiments, the system comprises a host including a host protocol stack and a host memory, and a network interface card that is communicatively connectable to the host. The A-GRO system is configured to: receive a packet from a network, parse the packet to a header and a payload, classify and map the packet into a particular flow based on contexts associated with a plurality of flows and the header, and move the header and the payload to separate queues associated with the particular flow in the host memory, without holding and stalling the packet in hardware of the NIC. By maintain packet coherence information including header chains, the A-GRO allows the host to skip processing the packets between the first and last headers in a GRO aggregation. The A-GRO system also improves mis-ordering packet handling.

Abstract: A system for using staging buffers in broadcast or multicast operations is disclosed. In some embodiments, the system comprises a server fabric adapter (SFA) communicatively coupled to a plurality of accelerators. The system is configured to provide a memory tier that is accessed by the plurality of accelerators; receive data in a send queue of the memory tier; establish an association between buffers of the send queue and one or more receive queues based on a pattern of sharing defined by one or more of the plurality of accelerators; and transmit the data to the one or more accelerators by sending the data from the send queue to the one or more receive queues based on the association.

Abstract: A multi-plane, multi-protocol memory switch system is disclosed. In some embodiments, a memory switch includes a plurality of switch ports, the memory switch connectable to one or more root complex (RC) devices through one or more respective switch ports of the plurality of switch ports, and the memory switch connectable to a set of endpoints through a set of other switch ports of the plurality of switch ports, wherein the set includes zero or multiple endpoints; a cacheline exchange engine configured to provide a data-exchange path between two endpoints and to map an address space of one endpoint to an address space of another endpoint; and a bulk data transfer engine configured to facilitate data-exchange between two endpoints as a source-destination data stream, one endpoint being designated a source address and another endpoint being designated a destination address.

Abstract: Exemplary systems and methods for visualization of data analysis are provided. In various embodiments, a method comprises accessing a database, analyzing the database to identify clusters of data, generating an interactive visualization comprising a plurality of nodes and a plurality of edges wherein a first node of the plurality of nodes represents a cluster and an edge of the plurality of edges represents an intersection of nodes of the plurality of nodes, selecting and dragging the first node in response to a user action, and reorienting the interactive visualization in response to the user action of selecting and dragging the first node.

Abstract: A system for admission and flow control is disclosed. In some embodiments, the system includes a switch for routing network traffic, having multiple classes of service (CoSs), from multiple ingress ports to one or more of multiple egress ports. The system also includes multiple ingress-level class of service queues (InCoS-Qs) and one or more egress-level class of service queues (EgCoS-Qs), each InCoS-Q and EgCoS-Q corresponding to one of CoSs. The switch is configured to detect congestion in a particular EgCoS-Q, corresponding to a particular CoS, the particular EgCoS-Q being associated with a particular host; identify an InCoS-Q corresponding to that particular CoS, and associated with that particular host; and block that InCoS-Q, while allowing routing of the network traffic from one or more InCoS-Qs corresponding to that particular CoS, the one or more InCoS-Qs corresponding to one or more other hosts.

Abstract: A system for admission and flow control is disclosed. In some embodiments, the system includes a switch for routing network traffic, having multiple classes of service (CoSs), from multiple ingress ports to one or more of multiple egress ports. The system also includes multiple ingress-level class of service queues (InCoS-Qs) and one or more egress-level class of service queues (EgCoS-Qs), each InCoS-Q and EgCoS-Q corresponding to one of CoSs. The switch is configured to detect congestion in a particular EgCoS-Q, corresponding to a particular CoS, the particular EgCoS-Q being associated with a particular host; identify an InCoS-Q corresponding to that particular CoS, and associated with that particular host; and block that InCoS-Q, while allowing routing of the network traffic from one or more InCoS-Qs corresponding to that particular CoS, the one or more InCoS-Qs corresponding to one or more other hosts.

Abstract: A multi-plane, multi-protocol memory switch system is disclosed. In some embodiments, a memory switch includes a plurality of switch ports, the memory switch connectable to one or more root complex (RC) devices through one or more respective switch ports of the plurality of switch ports, and the memory switch connectable to a set of endpoints through a set of other switch ports of the plurality of switch ports, wherein the set includes zero or multiple endpoints; a cacheline exchange engine configured to provide a data-exchange path between two endpoints and to map an address space of one endpoint to an address space of another endpoint; and a bulk data transfer engine configured to facilitate data-exchange between two endpoints as a source-destination data stream, one endpoint being designated a source address and another endpoint being designated a destination address.

Abstract: A computer is programmed to determine a training dataset that includes a plurality of images each including a first object and an object label, train a first machine learning program to identify first object parameters of the first objects in the plurality of images based on the object labels and a confidence level based on a standard deviation of a distribution of a plurality of identifications of the first object parameters, receive, from a second machine learning program, a plurality of second images each including a second object identified with a low confidence level, process the plurality of second images with the first machine learning program to identify the second object parameters with a corresponding second confidence level that is greater than a second confidence level, retrain the first machine learning program based on the identified second object parameters.

Abstract: An adaptive generic receive offload (A-GRO) system and method are disclosed. In some embodiments, the system comprises a host including a host protocol stack and a host memory, and a network interface card that is communicatively connectable to the host. The A-GRO system is configured to: receive a packet from a network, parse the packet to a header and a payload, classify and map the packet into a particular flow based on contexts associated with a plurality of flows and the header, and move the header and the payload to separate queues associated with the particular flow in the host memory, without holding and stalling the packet in hardware of the NIC. By maintain packet coherence information including header chains, the A-GRO allows the host to skip processing the packets between the first and last headers in a GRO aggregation. The A-GRO system also improves mis-ordering packet handling.

Abstract: A system for admission and flow control is disclosed. In some embodiments, the system includes a switch for routing network traffic, having multiple classes of service (CoSs), from multiple ingress ports to one or more of multiple egress ports. The system also includes multiple ingress-level class of service queues (InCoS-Qs) and one or more egress-level class of service queues (EgCoS-Qs), each InCoS-Q and EgCoS-Q corresponding to one of CoSs. The switch is configured to detect congestion in a particular EgCoS-Q, corresponding to a particular CoS, the particular EgCoS-Q being associated with a particular host; identify an InCoS-Q corresponding to that particular CoS, and associated with that particular host; and block that InCoS-Q, while allowing routing of the network traffic from one or more InCoS-Qs corresponding to that particular CoS, the one or more InCoS-Qs corresponding to one or more other hosts.

Abstract: The disclosure is generally directed to systems and methods for inference quality determination of a deep neural network (DNN) without requiring ground truth information for use in driver-assisted vehicles, including receiving an image frame from a source; applying a normal inference DNN model to the image frame to produce a first inference with a first bounding box using a normal inference DNN model; applying a deep inference DNN model to a plurality of filtered versions of the image frame to produce a plurality of deep inferences with a plurality of bounding boxes; comparing the plurality of bounding boxes to identify a cluster condition of the plurality of bounding boxes; and determining an inference quality of the image frame of the normal inference DNN model as a function of the cluster condition.

Abstract: An example method comprises receiving data points, determining at least one size of a plurality of subsets based on a constraint of at least one computation device or an analysis server, transferring each of the subsets to different computation devices, each computation device selecting a group of data points to generate a first sub-subset of landmarks, add non-landmark data points that have the farthest distance to the closest landmark to create an expanded sub-subset of landmarks, create an analysis landmark set based on a combination of expanded sub-subsets of expanded landmarks from different computation devices, perform a similarity function on the analysis landmark set, generate a cover of the mathematical reference space to create overlapping subsets, cluster the mapped landmark points based on the overlapping subsets, create a plurality of nodes, each node being based on the clustering, each landmark point being a member of at least one node.

Abstract: A computer includes a processor and a memory storing instructions executable by the processor to receive image data from a camera, generate a depth map from the image data, detect an object in the image data, apply a bounding box circumscribing the object to the depth map, mask the depth map by setting depth values for pixels in the bounding box in the depth map to a depth value of a closest pixel in the bounding box, and determine a distance to the object based on the masked depth map. The closest pixel is closest to the camera of the pixels in the bounding box.

Abstract: A multi-plane, multi-protocol memory switch system is disclosed. In some embodiments, a memory switch includes a plurality of switch ports, the memory switch connectable to one or more root complex (RC) devices through one or more respective switch ports of the plurality of switch ports, and the memory switch connectable to a set of endpoints through a set of other switch ports of the plurality of switch ports, wherein the set includes zero or multiple endpoints; a cacheline exchange engine configured to provide a data-exchange path between two endpoints and to map an address space of one endpoint to an address space of another endpoint; and a bulk data transfer engine configured to facilitate data-exchange between two endpoints as a source-destination data stream, one endpoint being designated a source address and another endpoint being designated a destination address.

Abstract: A first image can be acquired from a first sensor included in a vehicle and input to a deep neural network to determine a first bounding box for a first object. A second image can be acquired from the first sensor. Input latitudinal and longitudinal motion data from second sensors included in the vehicle corresponding to the time between inputting the first image and inputting the second image. A second bounding box can be determined by translating the first bounding box based on the latitudinal and longitudinal motion data. The second image can be cropped based on the second bounding box. The cropped second image can be input to the deep neural network to detect a second object. The first image, the first bounding box, the second image, and the second bounding box can be output.

Abstract: Exemplary systems and methods for visualization of data analysis are provided. In various embodiments, a method comprises accessing a database, analyzing the database to identify clusters of data, generating an interactive visualization comprising a plurality of nodes and a plurality of edges wherein a first node of the plurality of nodes represents a cluster and an edge of the plurality of edges represents an intersection of nodes of the plurality of nodes, selecting and dragging the first node in response to a user action, and reorienting the interactive visualization in response to the user action of selecting and dragging the first node.

Abstract: A computer includes a processor and a memory, and the memory stores instructions executable by the processor to receive an image frame from a camera, generate a feature map from the image frame, generate a depth map from the feature map, classify an object in the image frame based on the feature map, and estimate a distance to the object based on the depth map and based on an input to generating the feature map.

Abstract: In variants, a method for automated gamete selection can include: sampling a video of a scene having a plurality of gametes, tracking each gamete across successive images, and determining attribute values for a gamete, and selecting the gamete. The attribute values can be determined using a model trained to predict the attribute values for the gamete based on a video.

Abstract: In variants, a method for automated gamete selection can include: sampling a video of a scene having a plurality of gametes, tracking each gamete across successive images, and determining attribute values for a gamete, and selecting the gamete. The attribute values can be determined using a model trained to predict the attribute values for the gamete based on a video.