Abstract: A method comprises creating an electronic circuit design having a plurality of electronic components, simulating operation of the electronic circuit design, and creating a behavior model of the electronic circuit design. The method further comprises eliminating one or more data points created in the behavior model to generate a trimmed behavior model, generating a real number model based on the trimmed behavior model, the real number model comprising a plurality of weights, and generating a simulation model based on the plurality of weights.

Abstract: Hierarchical Power Management (HPM) architecture considers the limits of scaling on a power management controller, the autonomy at each die, and provides a unified view of the package to a platform. At a simplest level, HPM architecture has a supervisor and one or more supervisee power management units (PMUs) that communicate via at least two different communication fabrics. Each PMU can behave as a supervisor for a number of supervisee PMUs in a particular domain. HPM addresses these needs for products that comprise a collection of dice with varying levels of power and thermal management capabilities and needs. HPM serves as a unified mechanism than can span collection of dice of varying capability and function, which together form a traditional system-on-chip (SoC). HPM provides a basis for managing power and thermals across a diverse set of dice.

Abstract: Methods, systems, and apparatus, including computer programs encoded on computer storage media, for performing sequence generation tasks using binarized neural networks. The binarized neural network is an attention neural network configured to perform the task and the attention neural network includes a plurality of attention blocks, with each block including an attention block and a binarized feedforward block.

Abstract: Example aspects of the present disclosure provide a novel, resource-efficient approach for federated machine learning techniques with PTNs. The system can determine a first set of training parameters from a plurality of parameters of the global model. Additionally, the system can generate a random seed, using a random number generator, based on a set of frozen parameters. Moreover, the system can transmit, respectively to a plurality of client computing devices, a first set of training parameters and the random seed. Furthermore, the system can receive, respectively from the plurality of client computing devices, updates to one or more parameters in the first set of training parameters. Subsequently, the system can aggregate the updates to one or more parameters that are respectively received from the plurality of client computing devices. The system can modify one or more global parameters of the global model based on the aggregation.

Abstract: A system for detecting a computing device tethered to a mobile device is described. The system includes communicatively coupled the computing device to the mobile device with an IPv4 domain protocol. Additionally, the mobile device is communicatively coupled to the mobile data network with an IPv6 domain protocol. A network module, associated with a mobile data network, receives a DNS request packet from the mobile device. An IPv6 hostname and an IPv6 destination address is associated with the DNS response packet, when the DNS response packet is associated with the IPv6 address. Additionally, an IPv4 hostname and an IPv4 destination address is associated with the DNS response packet, when the DNS response packet is associated with the IPv4 address. A communication packet, generated by the mobile device, is also received by the network module. The communication packet includes a destination address and a hostname.

Abstract: A system for detecting a computing device tethered to a mobile device is described. The system includes communicatively coupled the computing device to the mobile device with an IPv4 domain protocol. Additionally, the mobile device is communicatively coupled to the mobile data network with an IPv6 domain protocol. A network module, associated with a mobile data network, receives a DNS request packet from the mobile device. An IPv6 hostname and an IPv6 destination address is associated with the DNS response packet, when the DNS response packet is associated with the IPv6 address. Additionally, an IPv4 hostname and an IPv4 destination address is associated with the DNS response packet, when the DNS response packet is associated with the IPv4 address. A communication packet, generated by the mobile device, is also received by the network module. The communication packet includes a destination address and a hostname.

Abstract: Systems and methods classify unknown devices communicating over a packet-switched network based on traffic-borne characteristics comprising packet parameters, flow parameters, and/or operating-system parameters. Embodiments utilize “self-learning” to optimize the level of classification accuracy.

Abstract: Systems and methods classify unknown devices communicating over a packet-switched network based on traffic-borne characteristics comprising packet parameters, flow parameters, and/or operating-system parameters. Embodiments utilize “self-learning” to optimize the level of classification accuracy.