Abstract: An integrated circuit device comprises pin resources, a memory controller circuit, a network interface controller circuit, and transmitter circuitry. The pin resources comprise pads coupled to off-chip pins of the integrated circuit device. The memory controller circuit comprises a first interface and the network interface controller circuit comprises a second interface. The transmitter circuitry is configurable to selectively couple either a first signal of the first interface or a second signal of the second interface to a first pad of the pin resources based on a pin distribution between the first interface and the second interface.

Abstract: An integrated circuit device comprises pin resources, a memory controller circuit, a network interface controller circuit, and transmitter circuitry. The pin resources comprise pads coupled to off-chip pins of the integrated circuit device. The memory controller circuit comprises a first interface and the network interface controller circuit comprises a second interface. The transmitter circuitry is configurable to selectively couple either a first signal of the first interface or a second signal of the second interface to a first pad of the pin resources based on a pin distribution between the first interface and the second interface.

Abstract: Systems and methods for anomaly detection and guided analysis using structural time-series model. A server may receive a request from a client to analyze a time-series data comprising a plurality of data points. A database of global calendars may be accessed. A structural time-series model may be built from the time-series data and the database of global calendars, the structural time-series model comprising a hidden structure and a plurality of probability distributions, each probability distribution corresponding to a data point. For each data point of the time-series data, a range of expected values is determined from a respective probability distribution, the range of expected values capturing a predefined percentage of the respective probability distribution. An anomaly is detected at a first data point of the time-series data responsive to comparing the first data point with a respective range of expected values. The anomaly is transmitted to the client for display with the time-series data.

Abstract: A system and method are provided for configuring a plurality of pin resources. The method includes identifying a plurality of pin resources of a primary application specific integrated circuit (ASIC) device and configuring the plurality of pin resources based on a pin distribution between a first interface and a second interface, where the first interface provides a first communication path between the primary ASIC device and a first device, and the second interface provides a second communication path between the primary ASIC device and a second device.

Abstract: A system and method are provided for configuring a plurality of pin resources. The method includes identifying a plurality of pin resources of a primary application specific integrated circuit (ASIC) device and configuring the plurality of pin resources based on a pin distribution between a first interface and a second interface, where the first interface provides a first communication path between the primary ASIC device and a first device, and the second interface provides a second communication path between the primary ASIC device and a second device.

Abstract: Performance of the machine learning technique is assessed using Bayesian analysis where previously grouped documents belonging to a machine-assigned class or cluster are presented to a human rater and the rater's assessment is fed to the Bayesian analysis processor that updates a Beta bionomial model with each document. The model represents the precision probability associated with the class or cluster under test. Monitoring the precision probability, the technique enforces a set of stopping rules corresponding to an acceptance/rejection assessment of the machine learning apparatus. A Markov Chain Monte Carlo process operates on the model to infuse the processing of each subsequent class or cluster with knowledge from those previously processed.