Abstract: In an example, an apparatus comprises a plurality of compute engines; and logic, at least partially including hardware logic, to detect a cache line conflict in a last-level cache (LLC) communicatively coupled to the plurality of compute engines; and implement context-based eviction policy to determine a cache way in the cache to evict in order to resolve the cache line conflict. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of compute engines; and logic, at least partially including hardware logic, to detect a cache line conflict in a last-level cache (LLC) communicatively coupled to the plurality of compute engines; and implement context-based eviction policy to determine a cache way in the cache to evict in order to resolve the cache line conflict. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to traverse a solution space, score a plurality of solutions to a scheduling deep learning network execution, and select a preferred solution from the plurality of solutions to implement the deep learning network. Other embodiments are also disclosed and claimed.

Abstract: Techniques are disclosed for updating a trusted software and another software, which may include receiving a software update package that comprises a trusted updated software component, a trusted updated software booting metadata, another updated software component, and another updated software booting metadata. The trusted updated software component may belong to an updated version of the trusted software, and the trusted software may have a certain trust level. The other updated software component may belong to an updated version of the other software, and the other software may have a trust level that is lower than the certain trust level. At least a part of the trusted updated software booting metadata may comprise retrieval information for retrieving, during a booting process, at least a portion of the other software booting metadata.

Abstract: Techniques are disclosed for updating a trusted software and another software, which may include receiving a software update package that comprises a trusted updated software component, a trusted updated software booting metadata, another updated software component, and another updated software booting metadata. The trusted updated software component may belong to an updated version of the trusted software, and the trusted software may have a certain trust level. The other updated software component may belong to an updated version of the other software, and the other software may have a trust level that is lower than the certain trust level. At least a part of the trusted updated software booting metadata may comprise retrieval information for retrieving, during a booting process, at least a portion of the other software booting metadata.

Abstract: In an example, an apparatus comprises logic, at least partially including hardware logic, to save one or more outputs of a deep learning neural network in a storage system of an autonomous vehicle and upload the one or more outputs to a remote server. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to traverse a solution space, score a plurality of solutions to a scheduling deep learning network execution, and select a preferred solution from the plurality of solutions to implement the deep learning network. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of compute engines; and logic, at least partially including hardware logic, to detect a cache line conflict in a last-level cache (LLC) communicatively coupled to the plurality of compute engines; and implement context-based eviction policy to determine a cache way in the cache to evict in order to resolve the cache line conflict. Other embodiments are also disclosed and claimed.

Abstract: Disclosed embodiments provide systems and methods that can be used as part of or in combination with autonomous navigation, autonomous driving, or driver assist technology features. As opposed to fully autonomous driving, driver assist technology may refer to any suitable technology to assist drivers in the navigation or control of their vehicles. In various embodiments, the system may include one or more cameras mountable in a vehicle and an associated processor that monitors the environment of the vehicle. In further embodiments, additional types of sensors can be mounted in the vehicle and can be used in the autonomous navigation or driver assist systems. These systems and methods may include the use of a shared cache that is shared by a group of processing units to improve analysis of images captured by the one or more cameras.

Abstract: Disclosed embodiments provide systems and methods that can be used as part of or in combination with autonomous navigation, autonomous driving, or driver assist technology features. As opposed to fully autonomous driving, driver assist technology may refer to any suitable technology to assist drivers in the navigation or control of their vehicles. In various embodiments, the system may include one or more cameras mountable in a vehicle and an associated processor that monitors the environment of the vehicle. In further embodiments, additional types of sensors can be mounted in the vehicle and can be used in the autonomous navigation or driver assist systems. These systems and methods may include the use of a shared cache that is shared by a group of processing units to improve analysis of images captured by the one or more cameras.

Abstract: Techniques are disclosed for updating a trusted software and another software, which may include receiving a software update package that comprises a trusted updated software component, a trusted updated software booting metadata, another updated software component, and another updated software booting metadata. The trusted updated software component may belong to an updated version of the trusted software, and the trusted software may have a certain trust level. The other updated software component may belong to an updated version of the other software, and the other software may have a trust level that is lower than the certain trust level. At least a part of the trusted updated software booting metadata may comprise retrieval information for retrieving, during a booting process, at least a portion of the other software booting metadata.

Abstract: In an example, an apparatus comprises a plurality of compute engines; and logic, at least partially including hardware logic, to detect a cache line conflict in a last-level cache (LLC) communicatively coupled to the plurality of compute engines; and implement context-based eviction policy to determine a cache way in the cache to evict in order to resolve the cache line conflict. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises logic, at least partially including hardware logic, to save one or more outputs of a deep learning neural network in a storage system of an autonomous vehicle and upload the one or more outputs to a remote server. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of compute engines; and logic, at least partially including hardware logic, to detect a cache line conflict in a last-level cache (LLC) communicatively coupled to the plurality of compute engines; and implement context-based eviction policy to determine a cache way in the cache to evict in order to resolve the cache line conflict. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises logic, at least partially including hardware logic, to save one or more outputs of a deep learning neural network in a storage system of an autonomous vehicle and upload the one or more outputs to a remote server. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises logic, at least partially including hardware logic, to save one or more outputs of a deep learning neural network in a storage system of an autonomous vehicle and upload the one or more outputs to a remote server. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of execution units comprising and logic, at least partially including hardware logic, to traverse a solution space, score a plurality of solutions to a scheduling deep learning network execution, and select a preferred solution from the plurality of solutions to implement the deep learning network. Other embodiments are also disclosed and claimed.

Abstract: In an example, an apparatus comprises a plurality of compute engines; and logic, at least partially including hardware logic, to detect a cache line conflict in a last-level cache (LLC) communicatively coupled to the plurality of compute engines; and implement context-based eviction policy to determine a cache way in the cache to evict in order to resolve the cache line conflict. Other embodiments are also disclosed and claimed.