Abstract: Apparatuses, methods and storage medium associated with computer-assisted or autonomous driving (CA/AD) vehicles are disclosed herein. In embodiments, CA/AD vehicles are members of a CA/AD vehicle social network (CASN) in which various CA/AD vehicles may form connections or relationships with one another. CA/AD vehicles that have an existing relationship or connection may share CASN information with one another. The CASN information may include authenticated and/or proprietary information. 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, methods and storage medium associated with computer-assisted or autonomous driving (CA/AD) vehicles are disclosed herein. In embodiments, CA/AD vehicles are members of a CA/AD vehicle social network (CASN) in which various CA/AD vehicles may form connections or relationships with one another. CA/AD vehicles that have an existing relationship or connection may share CASN information with one another. The CASN information may include authenticated and/or proprietary information. 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: 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 storage medium associated with computer-assisted or autonomous driving (CA/AD) vehicles are disclosed herein. In embodiments, CA/AD vehicles are members of a CA/AD vehicle social network (CASN) in which various CA/AD vehicles may form connections or relationships with one another. CA/AD vehicles that have an existing relationship or connection may share CASN information with one another. The CASN information may include authenticated and/or proprietary information. Other embodiments are also described and claimed.

Abstract: Apparatuses, methods and storage medium associated with computer-assisted or autonomous driving (CA/AD) vehicles are disclosed herein. In embodiments, CA/AD vehicles are members of a CA/AD vehicle social network (CASN) in which various CA/AD vehicles may form connections or relationships with one another. CA/AD vehicles that have an existing relationship or connection may share CASN information with one another. The CASN information may include authenticated and/or proprietary information. 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: Systems and methods are disclosed for buffering data using a multi-function, multi-protocol first-in-first-out (FIFO) circuit. For example, a data buffering apparatus is provided that includes a mode selection input and a FIFO circuit that is operative to buffer a data signal between a FIFO circuit input and a FIFO circuit output, wherein the FIFO circuit is configured in an operating mode responsive to the mode selection signal.

Abstract: One embodiment relates a method of receiving data from a multi-lane data link. The data is encoded with an FEC code having a block length. The data is FEC encoded at a bus width which is specified within particular constraints. One constraint is that the FEC encoder bus width in bits is an exact multiple of a number of bits per symbol in the data. Another constraint may be that the FEC code block length is an exact multiple of the FEC encoder bus width. Another constraint may be that the FEC encoder bus width is an exact multiple of a number of serial lanes of the multi-lane interface. Other embodiments and features are also disclosed.

Abstract: Apparatuses, methods and storage medium associated with computer-assisted or autonomous driving (CA/AD) vehicles are disclosed herein. In embodiments, CA/AD vehicles are members of a CA/AD vehicle social network (CASN) in which various CA/AD vehicles may form connections or relationships with one another. CA/AD vehicles that have an existing relationship or connection may share CASN information with one another. The CASN information may include authenticated and/or proprietary information. Other embodiments are also described and claimed.

Abstract: A configurable multi-protocol transceiver implemented in an integrated circuit (“IC”) includes configurable deskew circuitry. The transceiver has various configurable deskew settings to facilitate effectively adapting transmit and/or receive communications corresponding to a selected one of a plurality of high-speed communication protocols and/or adapt to different implementations in which a deskew block addresses either just static skew or both static and dynamic skew. Configurable circuitry is adapted to control an allowed data depth of a plurality of buffers. Configurable circuitry is adapted to control a deskew character transmit insertion frequency. A programmable state machine is adapted to control read and write pointers in accordance with selectable conditions for achieving an alignment lock condition.

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: 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: In embodiments, a computer-assisted or autonomous driving (CA/AD) vehicle includes a docking platform to receive one or more unmanned aerial vehicles (UAVs) of different types to dock with the CA/AD vehicle, each UAV to include at least one sensor directed to a configurable specific use of the CA/AD vehicle, and a UAV interface to communicate with the one or more docked UAVs, including to receive sensor data obtained by the one or more UAVs. In embodiments, the CA/AD vehicle is configured to a specific use, based at least in part on the one or more UAVs docked with the CA/AD vehicle.

Abstract: Systems and methods are disclosed for buffering data using a multi-function, multi-protocol first-in-first-out (FIFO) circuit. For example, a data buffering apparatus is provided that includes a mode selection input and a FIFO circuit that is operative to buffer a data signal between a FIFO circuit input and a FIFO circuit output, wherein the FIFO circuit is configured in an operating mode responsive to the mode selection signal.

Abstract: Systems and methods are disclosed for buffering data using a multi-function, multi-protocol first-in-first-out (FIFO) circuit. For example, a data buffering apparatus is provided that includes a mode selection input and a FIFO circuit that is operative to buffer a data signal between a FIFO circuit input and a FIFO circuit output, wherein the FIFO circuit is configured in an operating mode responsive to the mode selection signal.

Abstract: Embodiments include a configurable multi-protocol transceiver including configurable deskew circuitry. In one embodiment, configurable circuitry is adapted to control an allowed data depth of a plurality of buffers. In another embodiment, configurable circuitry is adapted to control a deskew character transmit insertion frequency. In another embodiment, a programmable state machine is adapted to control read and write pointers in accordance with selectable conditions for achieving an alignment lock condition. In another embodiment, configurable circuitry is adaptable to select between logic and routing resources in the transceiver and logic and routing resources in a core of the IC in which the transceiver is implemented for controlling at least certain deskew operations.

Abstract: One embodiment relates a method of receiving data from a multi-lane data link. The data is encoded with an FEC code having a block length. The data is FEC decoded at a bus width which is specified within particular constraints. One constraint is that the FEC decoder bus width in bits is an exact multiple of a number of bits per symbol in the data. Another constraint may be that the FEC code block length is an exact multiple of the FEC decoder bus width. Another constraint may be that the FEC decoder bus width is an exact multiple of a number of serial lanes of the multi-lane interface. Other embodiments and features are also disclosed.

Abstract: One embodiment relates to an integrated circuit configured to perform dynamic transmit equalization of a bi-directional lane. The integrated circuit including an interface between the physical coding and media access control circuitry, and an equalization control circuit which is external to the physical coding circuitry and which is configured to perform the dynamic transmit equalization using said interface. Another embodiment relates to a transceiver circuit which includes physical coding circuitry and media access control circuitry. The transceiver circuit further includes an interface between the physical coding circuitry and the media access control circuitry and an equalization controller which is external to the physical coding circuitry and which is configured to perform dynamic transmit equalization using said interface. The interface is configured to provide transmit coefficient data in a time-multiplexed signal format from the media access control circuitry to the physical coding circuitry.