Abstract: Systems and methods are provided to improve traditional chip processing. Using crossbar computations, the convolution layer can be flattened into vectors, and the vectors can be grouped into a matrix where each row or column is a flattened filter. Each submatrix of the input corresponding to a position of a convolution window is also flattened into a vector. The convolution is computed as the dot product of each input vector and the filter matrix. Using intra-crossbar computations, the unused space of the crossbars is used to store replicas of the filters matrices and the unused space in XIN is used to store more elements of the input. In inter-crossbar computations, the unused crossbars are used to store replicas of the filters matrices and the unused XINs are used to store more elements of the input. Then, the method performs multiple convolution iterations in a single step.

Abstract: A method for generating an employee benefit plan. The process collects employment data about employees of a plurality of business entities. The employment data comprises a number of dimensions of data collected from a number of sources. The process identifies a number of plan benefits for benefit plan for each of the business entities. The process determines metrics for the plan benefits during a given time interval. The process simultaneously models the plan benefits and the metrics for plan benefits to identify correlations among the dimensions of data and generalize rules for competitive benefit prediction. According to the modeling, the process predicts a number of competitive benefits for an employee benefit plan of a particular business entity based on the employment data of the particular business entity. The process generates the employee benefit plan for the particular business entity based on the number of competitive benefits.

Abstract: A network topology analysis and validation system and technique are provided. In some implementations, the system may obtain information in real-time mode or as an off-line data set. The information being representative of a defined network topology type for a computer network and communication connections for devices within the computer network. The computer network analysis may be performed on a subnet of a larger network. A communication topology of the computer network may be compared with the expected (defined) network topology using bipartite and bi-colorization techniques to classify nodes of the computer network. After classification, anomalous communication connections (not in conformance with a standard for the defined network topology) may be identified and colored for presentation to a system administrator. Anomalous communication connections may initiate an alert, event, or alarm, via a system administration monitoring system for real-time notification.

Abstract: Generating employee retention insights is provided. An analysis is performed, using an artificial intelligence component, of data in a completed digital off-boarding form corresponding to a particular employer and a type of a particular employee leaving the particular employer and information in data feeds from a plurality of different data sources. Insights are generated, using the artificial intelligence component, into employee retention for the type of the particular employee corresponding to the particular employer based on the analysis of the data in the completed digital off-boarding form and the information in the data feeds from the plurality of different data sources. A set of action steps are generated, using the artificial intelligence component, based on the insights and the data in the completed digital off-boarding form. One or more of the set of action steps are performed automatically.

Abstract: Systems and methods are provided to improve traditional chip processing. Using crossbar computations, the convolution layer can be flattened into vectors, and the vectors can be grouped into a matrix where each row or column is a flattened filter. Each submatrix of the input corresponding to a position of a convolution window is also flattened into a vector. The convolution is computed as the dot product of each input vector and the filter matrix. Using intra-crossbar computations, the unused space of the crossbars is used to store replicas of the filters matrices and the unused space in XIN is used to store more elements of the input. In inter-crossbar computations, the unused crossbars are used to store replicas of the filters matrices and the unused XINs are used to store more elements of the input. Then, the method performs multiple convolution iterations in a single step.

Abstract: A method for generating an employee benefit plan. The process collects employment data about employees of a plurality of business entities. The employment data comprises a number of dimensions of data collected from a number of sources. The process identifies a number of plan benefits for benefit plan for each of the business entities. The process determines metrics for the plan benefits during a given time interval. The process simultaneously models the plan benefits and the metrics for plan benefits to identify correlations among the dimensions of data and generalize rules for competitive benefit prediction. According to the modeling, the process predicts a number of competitive benefits for an employee benefit plan of a particular business entity based on the employment data of the particular business entity. The process generates the employee benefit plan for the particular business entity based on the number of competitive benefits.

Abstract: A network topology analysis and validation system and technique are provided. In some implementations, the system may obtain information in real-time mode or as an off-line data set. The information being representative of a defined network topology type for a computer network and communication connections for devices within the computer network. The computer network analysis may be performed on a subnet of a larger network. A communication topology of the computer network may be compared with the expected (defined) network topology using bipartite and bi-colorization techniques to classify nodes of the computer network. After classification, anomalous communication connections (not in conformance with a standard for the defined network topology) may be identified and colored for presentation to a system administrator. Anomalous communication connections may initiate an alert, event, or alarm, via a system administration monitoring system for real-time notification.

Abstract: Some examples provide a system to automatically discover network devices. The system enables a network device discovery protocol with a transmit mode and a receive mode on a network device. The system enables an auxiliary communication protocol on the network device. The system broadcasts the network device discovery protocol records from the network device including local neighbors and connectivity information. The system engine processes topological information using the auxiliary communication protocol and timing cycles to update age of a set of topology information records.