Abstract: Techniques are disclosed herein for reconfiguring reprogrammable hardware in an autonomous vehicle system. According to an embodiment, an autonomous driving system includes sensors and a configurable circuit having physical logic units. The autonomous driving system aggregates data observed from each of the sensors. The autonomous driving system detects a trigger indicative of a defect in the configurable circuit. The defect is identified as a function of the aggregated data. The autonomous driving system performs, in response to the trigger, a reconfiguration action on the configurable circuit to repair the defect.

Abstract: Techniques are disclosed herein for reconfiguring reprogrammable hardware in an autonomous vehicle system. According to an embodiment, an autonomous driving system includes sensors and a configurable circuit having physical logic units. The autonomous driving system aggregates data observed from each of the sensors. The autonomous driving system detects a trigger indicative of a defect in the configurable circuit. The defect is identified as a function of the aggregated data. The autonomous driving system performs, in response to the trigger, a reconfiguration action on the configurable circuit to repair the defect.

Abstract: Methods and apparatus to implement efficient memory storage in multi-die packages are disclosed. An example multi-die package includes a multi-die stack including a first die and a second die. The second die is stacked on the first die. The multi-die package further includes a third die adjacent the multi-die stack. The multi-die package also includes a silicon-based connector to communicatively couple the multi-die stack and the third die. The silicon-based connector includes at least one of a logic circuit or a memory circuit.

Abstract: Techniques are disclosed herein for reconfiguring reprogrammable hardware in an autonomous vehicle system. According to an embodiment, an autonomous driving system includes sensors and a configurable circuit having physical logic units. The autonomous driving system aggregates data observed from each of the sensors. The autonomous driving system detects a trigger indicative of a defect in the configurable circuit. The defect is identified as a function of the aggregated data. The autonomous driving system performs, in response to the trigger, a reconfiguration action on the configurable circuit to repair the defect.

Abstract: Techniques are disclosed herein for reconfiguring reprogrammable hardware in an autonomous vehicle system. According to an embodiment, an autonomous driving system includes sensors and a configurable circuit having physical logic units. The autonomous driving system aggregates data observed from each of the sensors. The autonomous driving system detects a trigger indicative of a defect in the configurable circuit. The defect is identified as a function of the aggregated data. The autonomous driving system performs, in response to the trigger, a reconfiguration action on the configurable circuit to repair the defect.

Abstract: Embodiments include apparatuses, methods, and systems to provide an automated loading device to a computer assisted or autonomous driving (CA/AD) vehicle. A loading service control device is to initiate a loading service to load one or more items into a storage space of a CA/AD vehicle, using an automated loading device. A CA/AD vehicle is to move to a loading area at an appointed time. A mechanical loading unit of an automated loading device is to place one or more items into a storage space of a CA/AD vehicle. A user device is to receive an input from a user, where the input includes information to generate a request to a loading service control device to load one or more items into a storage space of a CA/AD vehicle using an automated loading device. Other embodiments may also be described and claimed.

Abstract: Methods and apparatus to manage operation of variable-state computing devices using artificial intelligence are disclosed. An example computing device includes a hardware platform. The example computing device also includes an artificial intelligence (AI) engine to: determine a context of the device; and adjust an operation of the hardware platform based on an expected change in the context of the device. The adjustment modifies at least one of a computational efficiency of the device, a power efficiency of the device, or a memory response time of the device.

Abstract: Techniques are disclosed herein for reconfiguring reprogrammable hardware in an autonomous vehicle system. According to an embodiment, an autonomous driving system includes sensors and a configurable circuit having physical logic units. The autonomous driving system aggregates data observed from each of the sensors. The autonomous driving system detects a trigger indicative of a defect in the configurable circuit. The defect is identified as a function of the aggregated data. The autonomous driving system performs, in response to the trigger, a reconfiguration action on the configurable circuit to repair the defect.

Abstract: Embodiments include apparatuses, methods, and systems to provide an automated loading device to a computer assisted or autonomous driving (CA/AD) vehicle. A loading service control device is to initiate a loading service to load one or more items into a storage space of a CA/AD vehicle, using an automated loading device. A CA/AD vehicle is to move to a loading area at an appointed time. A mechanical loading unit of an automated loading device is to place one or more items into a storage space of a CA/AD vehicle. A user device is to receive an input from a user, where the input includes information to generate a request to a loading service control device to load one or more items into a storage space of a CA/AD vehicle using an automated loading device. Other embodiments may also be described and claimed.

Abstract: Methods and apparatus to implement efficient memory storage in multi-die packages are disclosed. An example multi-die package includes a multi-die stack including a first die and a second die. The second die is stacked on the first die. The multi-die package further includes a third die adjacent the multi-die stack. The multi-die package also includes a silicon-based connector to communicatively couple the multi-die stack and the third die. The silicon-based connector includes at least one of a logic circuit or a memory circuit.

Abstract: Methods and apparatus to manage operation of variable-state computing devices using artificial intelligence are disclosed. An example computing device includes a hardware platform. The example computing device also includes an artificial intelligence (AI) engine to: determine a context of the device; and adjust an operation of the hardware platform based on an expected change in the context of the device. The adjustment modifies at least one of a computational efficiency of the device, a power efficiency of the device, or a memory response time of the device.