Abstract: In various examples, activation criteria and/or braking profiles corresponding to automatic emergency braking (AEB) systems and/or collision mitigation warning (CMW) systems may be determined using sensor data representative of an environment to a front, side, and/or rear of a vehicle. For example, activation criteria for triggering an AEB system and/or CMW system may be adjusted by leveraging the availability of additional information with regards to the surrounding environment of a vehicle—such as the presence of a trailing vehicle. In addition, the braking profile for the AEB activation may be adjusted based on information about the presence of and/or location of vehicles to the front, rear, and/or side of the vehicle. By adjusting the activation criteria and/or braking profiles of an AEB system, the potential for collisions with dynamic objects in the environment is reduced and the overall safety of the vehicle and its passengers is increased.

Abstract: Autonomous driving is one of the world's most challenging computational problems. Very large amounts of data from cameras, RADARs, LIDARs, and HD-Maps must be processed to generate commands to control the car safely and comfortably in real-time. This challenging task requires a dedicated supercomputer that is energy-efficient and low-power, complex high-performance software, and breakthroughs in deep learning AI algorithms. To meet this task, the present technology provides advanced systems and methods that facilitate autonomous driving functionality, including a platform for autonomous driving Levels 3, 4, and/or 5. In preferred embodiments, the technology provides an end-to-end platform with a flexible architecture, including an architecture for autonomous vehicles that leverages computer vision and known ADAS techniques, providing diversity and redundancy, and meeting functional safety standards.

Abstract: In various examples, activation criteria and/or braking profiles corresponding to automatic emergency braking (AEB) systems and/or collision mitigation warning (CMW) systems may be determined using sensor data representative of an environment to a front, side, and/or rear of a vehicle. For example, activation criteria for triggering an AEB system and/or CMW system may be adjusted by leveraging the availability of additional information with regards to the surrounding environment of a vehicle—such as the presence of a trailing vehicle. In addition, the braking profile for the AEB activation may be adjusted based on information about the presence of and/or location of vehicles to the front, rear, and/or side of the vehicle. By adjusting the activation criteria and/or braking profiles of an AEB system, the potential for collisions with dynamic objects in the environment is reduced and the overall safety of the vehicle and its passengers is increased.

Abstract: Autonomous driving is one of the world's most challenging computational problems. Very large amounts of data from cameras, RADARs, LIDARs, and HD-Maps must be processed to generate commands to control the car safely and comfortably in real-time. This challenging task requires a dedicated supercomputer that is energy-efficient and low-power, complex high-performance software, and breakthroughs in deep learning AI algorithms. To meet this task, the present technology provides advanced systems and methods that facilitate autonomous driving functionality, including a platform for autonomous driving Levels 3, 4, and/or 5. In preferred embodiments, the technology provides an end-to-end platform with a flexible architecture, including an architecture for autonomous vehicles that leverages computer vision and known ADAS techniques, providing diversity and redundancy, and meeting functional safety standards.

Abstract: Autonomous driving is one of the world's most challenging computational problems. Very large amounts of data from cameras, RADARs, LIDARs, and HD-Maps must be processed to generate commands to control the car safely and comfortably in real-time. This challenging task requires a dedicated supercomputer that is energy-efficient and low-power, complex high-performance software, and breakthroughs in deep learning AI algorithms. To meet this task, the present technology provides advanced systems and methods that facilitate autonomous driving functionality, including a platform for autonomous driving Levels 3, 4, and/or 5. In preferred embodiments, the technology provides an end-to-end platform with a flexible architecture, including an architecture for autonomous vehicles that leverages computer vision and known ADAS techniques, providing diversity and redundancy, and meeting functional safety standards.

Abstract: In various examples, activation criteria and/or braking profiles corresponding to automatic emergency braking (AEB) systems and/or collision mitigation warning (CMW) systems may be determined using sensor data representative of an environment to a front, side, and/or rear of a vehicle. For example, activation criteria for triggering an AEB system and/or CMW system may be adjusted by leveraging the availability of additional information with regards to the surrounding environment of a vehicle—such as the presence of a trailing vehicle. In addition, the braking profile for the AEB activation may be adjusted based on information about the presence of and/or location of vehicles to the front, rear, and/or side of the vehicle. By adjusting the activation criteria and/or braking profiles of an AEB system, the potential for collisions with dynamic objects in the environment is reduced and the overall safety of the vehicle and its passengers is increased.

Abstract: In various examples, activation criteria and/or braking profiles corresponding to automatic emergency braking (AEB) systems and/or collision mitigation warning (CMW) systems may be determined using sensor data representative of an environment to a front, side, and/or rear of a vehicle. For example, activation criteria for triggering an AEB system and/or CMW system may be adjusted by leveraging the availability of additional information with regards to the surrounding environment of a vehicle—such as the presence of a trailing vehicle. In addition, the braking profile for the AEB activation may be adjusted based on information about the presence of and/or location of vehicles to the front, rear, and/or side of the vehicle. By adjusting the activation criteria and/or braking profiles of an AEB system, the potential for collisions with dynamic objects in the environment is reduced and the overall safety of the vehicle and its passengers is increased.

Abstract: In various examples, activation criteria and/or braking profiles corresponding to automatic emergency braking (AEB) systems and/or collision mitigation warning (CMW) systems may be determined using sensor data representative of an environment to a front, side, and/or rear of a vehicle. For example, activation criteria for triggering an AEB system and/or CMW system may be adjusted by leveraging the availability of additional information with regards to the surrounding environment of a vehicle—such as the presence of a trailing vehicle. In addition, the braking profile for the AEB activation may be adjusted based on information about the presence of and/or location of vehicles to the front, rear, and/or side of the vehicle. By adjusting the activation criteria and/or braking profiles of an AEB system, the potential for collisions with dynamic objects in the environment is reduced and the overall safety of the vehicle and its passengers is increased.

Abstract: Autonomous driving is one of the world's most challenging computational problems. Very large amounts of data from cameras, RADARs, LIDARs, and HD-Maps must be processed to generate commands to control the car safely and comfortably in real-time. This challenging task requires a dedicated supercomputer that is energy-efficient and low-power, complex high-performance software, and breakthroughs in deep learning AI algorithms. To meet this task, the present technology provides advanced systems and methods that facilitate autonomous driving functionality, including a platform for autonomous driving Levels 3, 4, and/or 5. In preferred embodiments, the technology provides an end-to-end platform with a flexible architecture, including an architecture for autonomous vehicles that leverages computer vision and known ADAS techniques, providing diversity and redundancy, and meeting functional safety standards.

Abstract: A device comprises a plurality of interface circuits configured for communicating between a semantic processing unit and a memory and a selection circuit for selecting an interface circuit allocated to a semantic processing unit for processing a data operation request in the memory.

Abstract: A device performs lookup functions for a semantic processing unit. The device comprises a plurality of interface circuits for receiving data operation requests from the semantic processing unit. The device comprises a buffer for allocating an interface circuit to a semantic processing unit having a data operation request. A selection circuit, coupled between the plurality of interface circuits and a memory unit, selects an allocated circuit for accessing the memory unit to process the data operation request.

Abstract: A memory subsystem includes multiple different caches configured for different types of data transfer operations between one or more processing units and a main memory. The different caches can include a first general cache configured for general random memory accesses, a software controlled cache used for controlling cache operations for different processing devices accessing the same data, and a streaming cache configured for large packet data memory accesses. An arbiter may be used for arbitrating requests by the multiple different caches for accessing the main memory.

Abstract: A method of bonding layers to form a structure, comprises curing a first adhesive while squeezing a first layer and a multilayer structure together between a first backing and a second backing. The multilayer structure comprises a substrate and a second layer, and the first adhesive is between and in contact with the first layer and the second layer. Furthermore, the first layer and the second layer each have a thickness of at most 100 ?m, and at least one of the first backing and the second backing comprises a first elastic polymer.

Abstract: A device has an input port to allow the device to receive data. The device also has a parser to parse the data in response to symbols in a parser stack, determine when a symbol is a debug non-terminal symbol, and notify the device via an interrupt. The interrupt causes the device to gather information about the state of the parser at the time of encountering the non-terminal symbol.

Abstract: A system and method include identifying a conditional skip instruction, determining when a conditional skip instruction is satisfied according to a result of an associated compare function, and skipping a fixed-number of the instructions defined by the conditional skip instruction when the conditional skip function is satisfied.

Abstract: A dispatcher module has a queue to store task requests. The dispatcher also has a task arbiter to select a current task for assignment from the task requests and a unit arbiter to identify and assign the task to an available processing unit, such that the current task is not assigned to a previously-assigned processing unit.

Abstract: An NMR system comprises an NMR probe comprising multiple NMR detection sites. Each of the multiple NMR detection sites comprises a sample holding void and an associated NMR microcoil. The NMR system further comprises a controllable fluid router operative to direct fluid sample to the multiple NMR detection sites.

Abstract: A device comprises a plurality of interface circuits configured for communicating between a semantic processing unit and a memory and a selection circuit for selecting an interface circuit allocated to a semantic processing unit for processing a data operation request in the memory.

Abstract: A device performs lookup functions for a semantic processing unit. The device comprises a plurality of interface circuits for receiving data operation requests from the semantic processing unit. The device comprises a buffer for allocating an interface circuit to a semantic processing unit having a data operation request. A selection circuit, coupled between the plurality of interface circuits and a memory unit, selects an allocated circuit for accessing the memory unit to process the data operation request.

Abstract: A device comprises a plurality of interface circuits for communicating between a semantic processor and a memory. Each interface circuit is configured for receiving lookup requests from the semantic processor. The device further comprises a buffer for allocating an interface circuit, if available, to the semantic processor. The allocated interface circuit is selected to access the memory for processing the lookup request.