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, systems, and devices that support fuseload architectures for system-on-chip (SoC) reconfiguration and repurposing are described. Trim data may be loaded from fuses to registers on a die based on a fuse header. For example, a set of registers coupled with a set of fuses on the die may be identified, where the set of fuses may store trim data to be copied to the registers as part of a fuseload procedure. In such cases, one or more fuse headers may be identified within the trim data, and each fuse header may correspond to a fuse group that includes a subset of fuses. Based on one or more subfields within a fuse header, a mapping between fuse addresses and register addresses may be determined, and the trim data from each fuse group may be copied into a set of registers based on the mapping.

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: 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, systems, and devices that support fuseload architectures for system-on-chip (SoC) reconfiguration and repurposing are described. Trim data may be loaded from fuses to registers on a die based on a fuse header. For example, a set of registers coupled with a set of fuses on the die may be identified, where the set of fuses may store trim data to be copied to the registers as part of a fuseload procedure. In such cases, one or more fuse headers may be identified within the trim data, and each fuse header may correspond to a fuse group that includes a subset of fuses. Based on one or more subfields within a fuse header, a mapping between fuse addresses and register addresses may be determined, and the trim data from each fuse group may be copied into a set of registers based on the mapping.

Abstract: Methods, systems, and devices that support fuseload architectures for system-on-chip (SoC) reconfiguration and repurposing are described. Trim data may be loaded from fuses to registers on a die based on a fuse header. For example, a set of registers coupled with a set of fuses on the die may be identified, where the set of fuses may store trim data to be copied to the registers as part of a fuseload procedure. In such cases, one or more fuse headers may be identified within the trim data, and each fuse header may correspond to a fuse group that includes a subset of fuses. Based on one or more subfields within a fuse header, a mapping between fuse addresses and register addresses may be determined, and the trim data from each fuse group may be copied into a set of registers based on the mapping.

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, 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: 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, 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, systems, and devices that support fuseload architectures for system-on-chip (SoC) reconfiguration and repurposing are described. Trim data may be loaded from fuses to registers on a die based on a fuse header. For example, a set of registers coupled with a set of fuses on the die may be identified, where the set of fuses may store trim data to be copied to the registers as part of a fuseload procedure. In such cases, one or more fuse headers may be identified within the trim data, and each fuse header may correspond to a fuse group that includes a subset of fuses. Based on one or more subfields within a fuse header, a mapping between fuse addresses and register addresses may be determined, and the trim data from each fuse group may be copied into a set of registers based on the mapping.

Abstract: Methods, systems, and devices that support fuseload architectures for system-on-chip (SoC) reconfiguration and repurposing are described. Trim data may be loaded from fuses to registers on a die based on a fuse header. For example, a set of registers coupled with a set of fuses on the die may be identified, where the set of fuses may store trim data to be copied to the registers as part of a fuseload procedure. In such cases, one or more fuse headers may be identified within the trim data, and each fuse header may correspond to a fuse group that includes a subset of fuses. Based on one or more subfields within a fuse header, a mapping between fuse addresses and register addresses may be determined, and the trim data from each fuse group may be copied into a set of registers based on the mapping.

Abstract: The present disclosure relates to memory array access control. An apparatus includes partition control circuitry to control at least one partition of a memory array, the at least one partition control circuitry also to receive a controlled clock signal to enable execution of a legitimate memory access command and to generate an active/idle signal having an active state when executing the legitimate memory access command and an idle state when executing the legitimate memory access command is complete; wherein the clock signal is disabled when the active/idle signal is in an idle state.

Abstract: In one embodiment, an apparatus comprises a plurality of memory partitions, each memory partition comprising an array of 3D crosspoint memory, and a storage device controller comprising a memory comprising memory cells to store addresses of replacement memory partitions and addresses of unusable memory partitions and a partition address translation engine coupled to the memory, the partition address translation engine comprising logic to determine whether to redirect a memory access command received from a host computing device to a replacement memory partition based on the contents of the memory.

Abstract: Methods, systems, and devices that support fuseload architectures for system-on-chip (SoC) reconfiguration and repurposing are described. Trim data may be loaded from fuses to registers on a die based on a fuse header. For example, a set of registers coupled with a set of fuses on the die may be identified, where the set of fuses may store trim data to be copied to the registers as part of a fuseload procedure. In such cases, one or more fuse headers may be identified within the trim data, and each fuse header may correspond to a fuse group that includes a subset of fuses. Based on one or more subfields within a fuse header, a mapping between fuse addresses and register addresses may be determined, and the trim data from each fuse group may be copied into a set of registers based on the mapping.

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: The present disclosure relates to memory array access control. An apparatus includes partition control circuitry to control at least one partition of a memory array, the at least one partition control circuitry also to receive a controlled clock signal to enable execution of a legitimate memory access command and to generate an active/idle signal having an active state when executing the legitimate memory access command and an idle state when executing the legitimate memory access command is complete; wherein the clock signal is disabled when the active/idle signal is in an idle state.

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.