Abstract: An electronic computing device with a self-shielding antenna. An electronic computing device may include a frame, an antenna, and an antenna shielding. The frame includes a top cover and a bottom cover. Electronic components are included in a space formed between the top cover and the bottom cover. The antenna is for wireless transmission and reception and included in the frame near an edge of the frame. The antenna shielding is disposed around the antenna for providing electro-magnetic shielding from radio frequency (RE) noises generated from the electronic components included in the frame. The antenna shielding may be a metal wall disposed between the top cover and the bottom cover around the antenna. The frame may be a metallic frame and may include a cut-out in the top cover and the bottom cover above and below the antenna, and a non-metallic cover may be provided in the cut-out.

Abstract: Examples relate to a handheld device, a dock for a handheld device, and to corresponding methods and systems. The handheld device comprises a main unit comprising a display of the handheld device. The handheld device comprises two input controllers being non-removably attached to the main unit via an extension mechanism. The extension mechanism is configured such, that the two input controllers are movable from a retracted configuration to an extended configuration.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to improve data training of a machine learning model using a field-programmable gate array (FPGA). An example system includes one or more computation modules, each of the one or more computation modules associated with a corresponding user, the one or more computation modules training first neural networks using data associated with the corresponding users, and FPGA to obtain a first set of parameters from each of the one or more computation modules, the first set of parameters associated with the first neural networks, configure a second neural network based on the first set of parameters, execute the second neural network to generate a second set of parameters, and transmit the second set of parameters to the first neural networks to update the first neural networks.

Abstract: Methods, apparatus, systems, and articles of manufacture for allocating a workload to an accelerator using machine learning are disclosed. An example apparatus includes a workload attribute determiner to identify a first attribute of a first workload and a second attribute of a second workload. An accelerator selection processor causes at least a portion of the first workload to be executed by at least two accelerators, accesses respective performance metrics corresponding to execution of the first workload by the at least two accelerators, and selects a first accelerator of the at least two accelerators based on the performance metrics. A neural network trainer trains a machine learning model based on an association between the first accelerator and the first attribute of the first workload. A neural network processor processes, using the machine learning model, the second attribute to select one of the at least two accelerators to execute the second workload.

Abstract: An electronic computing device with a self-shielding antenna. An electronic computing device may include a frame, an antenna, and an antenna shielding. The frame includes a top cover and a bottom cover. Electronic components are included in a space formed between the top cover and the bottom cover. The antenna is for wireless transmission and reception and included in the frame near an edge of the frame. The antenna shielding is disposed around the antenna for providing electro-magnetic shielding from radio frequency (RE) noises generated from the electronic components included in the frame. The antenna shielding may be a metal wall disposed between the top cover and the bottom cover around the antenna. The frame may be a metallic frame and may include a cut-out in the top cover and the bottom cover above and below the antenna, and a non-metallic cover may be provided in the cut-out.

Abstract: Methods, apparatus, systems, and articles of manufacture for allocating a workload to an accelerator using machine learning are disclosed. An example apparatus includes a workload attribute determiner to identify a first attribute of a first workload and a second attribute of a second workload. An accelerator selection processor causes at least a portion of the first workload to be executed by at least two accelerators, accesses respective performance metrics corresponding to execution of the first workload by the at least two accelerators, and selects a first accelerator of the at least two accelerators based on the performance metrics. A neural network trainer trains a machine learning model based on an association between the first accelerator and the first attribute of the first workload. A neural network processor processes, using the machine learning model, the second attribute to select one of the at least two accelerators to execute the second workload.

Abstract: Examples relate to a handheld device, a dock for a handheld device, and to corresponding methods and systems. The handheld device comprises a main unit comprising a display of the handheld device. The handheld device comprises two input controllers being non-removably attached to the main unit via an extension mechanism. The extension mechanism is configured such, that the two input controllers are movable from a retracted configuration to an extended configuration.

Abstract: Methods, apparatus, systems, and articles of manufacture for allocating a workload to an accelerator using machine learning are disclosed. An example apparatus includes a workload attribute determiner to identify a first attribute of a first workload and a second attribute of a second workload. An accelerator selection processor causes at least a portion of the first workload to be executed by at least two accelerators, accesses respective performance metrics corresponding to execution of the first workload by the at least two accelerators, and selects a first accelerator of the at least two accelerators based on the performance metrics. A neural network trainer trains a machine learning model based on an association between the first accelerator and the first attribute of the first workload. A neural network processor processes, using the machine learning model, the second attribute to select one of the at least two accelerators to execute the second workload.

Abstract: Apparatuses, methods, and systems associated with safety-related decision making reporting and regulation of computer-assisted or autonomous driving (CA/AD) vehicles are disclosed herein. In some embodiments, an apparatus includes a safety-related decision making reporting unit, disposed in a CA/AD vehicle, to collect data about driving behavior of the CA/AD vehicle and to determine whether the collected data is related to a safety-related decision making rule. In embodiments, the collected data is to be reported to a remote organization associated with regulating the safety-related decision making rule. In some embodiments, a computing device or server associated with regulating safety-related decision making rules receives the collected data from the CA/AD vehicle and/or manufacturers of the CA/AD vehicle. In embodiments, the computing device analyzes the collected data to modify or generate a safety-decision making rule. Other embodiments are also described and claimed.

Abstract: Methods, apparatus, systems, and articles of manufacture for allocating a workload to an accelerator using machine learning are disclosed. An example apparatus includes a workload attribute determiner to identify a first attribute of a first workload and a second attribute of a second workload. An accelerator selection processor causes at least a portion of the first workload to be executed by at least two accelerators, accesses respective performance metrics corresponding to execution of the first workload by the at least two accelerators, and selects a first accelerator of the at least two accelerators based on the performance metrics. A neural network trainer trains a machine learning model based on an association between the first accelerator and the first attribute of the first workload. A neural network processor processes, using the machine learning model, the second attribute to select one of the at least two accelerators to execute the second workload.

Abstract: Apparatuses, methods, and systems associated with safety-related decision making reporting and regulation of computer-assisted or autonomous driving (CA/AD) vehicles are disclosed herein. In some embodiments, an apparatus includes a safety-related decision making reporting unit, disposed in a CA/AD vehicle, to collect data about driving behavior of the CA/AD vehicle and to determine whether the collected data is related to a safety-related decision making rule. In embodiments, the collected data is to be reported to a remote organization associated with regulating the safety-related decision making rule. In some embodiments, a computing device or server associated with regulating safety-related decision making rules receives the collected data from the CA/AD vehicle and/or manufacturers of the CA/AD vehicle. In embodiments, the computing device analyzes the collected data to modify or generate a safety-decision making rule. Other embodiments are also described and claimed.

Abstract: Methods, apparatus, systems, and articles of manufacture are disclosed to improve data training of a machine learning model using a field-programmable gate array (FPGA). An example system includes one or more computation modules, each of the one or more computation modules associated with a corresponding user, the one or more computation modules training first neural networks using data associated with the corresponding users, and FPGA to obtain a first set of parameters from each of the one or more computation modules, the first set of parameters associated with the first neural networks, configure a second neural network based on the first set of parameters, execute the second neural network to generate a second set of parameters, and transmit the second set of parameters to the first neural networks to update the first neural networks.

Abstract: Apparatuses, systems, and methods associated with an infrastructure for re-charging/refueling and exchanging data with vehicles are disclosed herein. In embodiments, an apparatus for servicing a computer-assisted or autonomous driving (CA/AD) vehicle may include an energy re-supply unit to re-charge or re-fuel the vehicle, a communication unit to exchange data with in-vehicle electronics of the vehicle, a memory device coupled to the communication unit to store data received from the in-vehicle electronics, a networking unit to couple the memory device to a network, the networking unit to provide for exchange of data between the memory device and another device coupled to the network, and a control unit coupled to the energy re-supply unit, the communication unit, the memory device and the networking unit to control operations of the energy re-supply unit, the communication unit, the memory device and the networking unit. Other embodiments may be described and/or claimed.