Abstract: An autonomous vehicle is provided that includes one or more processors configured to provide a local compute manager to manage execution of compute workloads associated with the autonomous vehicle. The local compute manager can perform various compute operations, including receiving offload of compute operations from to other compute nodes and offloading compute operations to other compute notes, where the other compute nodes can be other autonomous vehicles. The local compute manager can also facilitate autonomous navigation functionality.

Abstract: Example methods and apparatus to generate anomaly detection datasets are disclosed. An example method to generate an anomaly detection dataset for training a machine learning model to detect real world anomalies includes receiving a user definition of an anomaly generator function, executing, with a processor, the anomaly generator function to generate user-defined anomaly data, and combining the user-defined anomaly data with nominal data to generate the anomaly detection dataset.

Abstract: Example methods and apparatus to generate anomaly detection datasets are disclosed. An example method to generate an anomaly detection dataset for training a machine learning model to detect real world anomalies includes receiving a user definition of an anomaly generator function, executing, with a processor, the anomaly generator function to generate user-defined anomaly data, and combining the user-defined anomaly data with nominal data to generate the anomaly detection dataset.

Abstract: An autonomous vehicle is provided that includes one or more processors configured to provide a local compute manager to manage execution of compute workloads associated with the autonomous vehicle. The local compute manager can perform various compute operations, including receiving offload of compute operations from to other compute nodes and offloading compute operations to other compute notes, where the other compute nodes can be other autonomous vehicles. The local compute manager can also facilitate autonomous navigation functionality.

Abstract: One embodiment provides for a computing device within an autonomous vehicle, the compute device comprising a wireless network device to enable a wireless data connection with an autonomous vehicle network, a set of multiple processors including a general-purpose processor and a general-purpose graphics processor, the set of multiple processors to execute a compute manager to manage execution of compute workloads associated with the autonomous vehicle, the compute workload associated with autonomous operations of the autonomous vehicle, and offload logic configured to execute on the set of multiple processors, the offload logic to determine to offload one or more of the compute workloads to one or more autonomous vehicles within range of the wireless network device.

Abstract: One embodiment provides for a computing device within an autonomous vehicle, the compute device comprising a wireless network device to enable a wireless data connection with an autonomous vehicle network, a set of multiple processors including a general-purpose processor and a general-purpose graphics processor, the set of multiple processors to execute a compute manager to manage execution of compute workloads associated with the autonomous vehicle, the compute workload associated with autonomous operations of the autonomous vehicle, and offload logic configured to execute on the set of multiple processors, the offload logic to determine to offload one or more of the compute workloads to one or more autonomous vehicles within range of the wireless network device.

Abstract: According to an example, an instruction to run a kernel of an application on an apparatus having a first processing unit integrated with a second processing unit may be received. In addition, an application profile for the application at a runtime of the application kernel on the second processing unit may be created, in which the application profile identifies an affinity of the application kernel to be run on either the first processing unit or the second processing unit, and identifies a characterization of an input data set of the application. The application profile may also be stored in a data store.

Abstract: One embodiment provides for a computing device within an autonomous vehicle, the compute device comprising a wireless network device to enable a wireless data connection with an autonomous vehicle network, a set of multiple processors including a general-purpose processor and a general-purpose graphics processor, the set of multiple processors to execute a compute manager to manage execution of compute workloads associated with the autonomous vehicle, the compute workload associated with autonomous operations of the autonomous vehicle, and offload logic configured to execute on the set of multiple processors, the offload logic to determine to offload one or more of the compute workloads to one or more autonomous vehicles within range of the wireless network device.

Abstract: Example methods and apparatus to generate anomaly detection datasets are disclosed. An example method to generate an anomaly detection dataset for training a machine learning model to detect real world anomalies includes receiving a user definition of an anomaly generator function, executing, with a processor, the anomaly generator function to generate user-defined anomaly data, and combining the user-defined anomaly data with nominal data to generate the anomaly detection dataset.

Abstract: One embodiment provides for a computing device within an autonomous vehicle, the compute device comprising a wireless network device to enable a wireless data connection with an autonomous vehicle network, a set of multiple processors including a general-purpose processor and a general-purpose graphics processor, the set of multiple processors to execute a compute manager to manage execution of compute workloads associated with the autonomous vehicle, the compute workload associated with autonomous operations of the autonomous vehicle, and offload logic configured to execute on the set of multiple processors, the offload logic to determine to offload one or more of the compute workloads to one or more autonomous vehicles within range of the wireless network device.

Abstract: According to an example, an instruction to run a kernel of an application on an apparatus having a first processing unit integrated with a second processing unit may be received. In addition, an application profile for the application at a runtime of the application kernel on the second processing unit may be created, in which the application profile identifies an affinity of the application kernel to be run on either the first processing unit or the second processing unit, and identifies a characterization of an input data set of the application. The application profile may also be stored in a data store.