Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices for autonomous vehicle operation are provided. For example, a computing system can receive object data that includes portions of sensor data. The computing system can determine, in a first stage of a multiple stage classification using hardware components, one or more first stage characteristics of the portions of sensor data based on a first machine-learned model. In a second stage of the multiple stage classification, the computing system can determine second stage characteristics of the portions of sensor data based on a second machine-learned model. The computing system can generate an object output based on the first stage characteristics and the second stage characteristics. The object output can include indications associated with detection of objects in the portions of sensor data.

Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices for autonomous vehicle operation are provided. For example, a computing system can receive object data that includes portions of sensor data. The computing system can determine, in a first stage of a multiple stage classification using hardware components, one or more first stage characteristics of the portions of sensor data based on a first machine-learned model. In a second stage of the multiple stage classification, the computing system can determine second stage characteristics of the portions of sensor data based on a second machine-learned model. The computing system can generate an object output based on the first stage characteristics and the second stage characteristics. The object output can include indications associated with detection of objects in the portions of sensor data.

Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices for autonomous vehicle operation are provided. For example, a computing system can receive object data that includes portions of sensor data. The computing system can determine, in a first stage of a multiple stage classification using hardware components, one or more first stage characteristics of the portions of sensor data based on a first machine-learned model. In a second stage of the multiple stage classification, the computing system can determine second stage characteristics of the portions of sensor data based on a second machine-learned model. The computing system can generate an object output based on the first stage characteristics and the second stage characteristics. The object output can include indications associated with detection of objects in the portions of sensor data.

Abstract: In one example embodiment, a computer-implemented method includes obtaining sensor data from a sensor, the sensor data corresponding to an image frame, and the sensor data including a first portion that corresponds to a portion of the image frame. The method includes pipelining the first portion of the sensor data into a machine-learned model before the sensor data corresponding to the entire image frame is transferred from the sensor to a memory device, to perform one or more inference operations on the first portion of the sensor data. The method includes generating as an output of the machine-learned model, in response to pipelining the sensor data corresponding to each portion of the image frame into the machine-learned model, a detection or classification of the one or more objects indicated within the sensor data.

Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices for autonomous vehicle operation are provided. For example, a computing system can receive object data that includes portions of sensor data. The computing system can determine, in a first stage of a multiple stage classification using hardware components, one or more first stage characteristics of the portions of sensor data based on a first machine-learned model. In a second stage of the multiple stage classification, the computing system can determine second stage characteristics of the portions of sensor data based on a second machine-learned model. The computing system can generate an object output based on the first stage characteristics and the second stage characteristics. The object output can include indications associated with detection of objects in the portions of sensor data.

Abstract: Image processing systems can include one or more cameras configured to obtain image data, one or more memory devices configured to store a classification model that classifies image features within the image data as including or not including detected objects, and a field programmable gate array (FPGA) device coupled to the one or more cameras. The FPGA device is configured to implement one or more image processing pipelines for image transformation and object detection. The one or more image processing pipelines can generate a multi-scale image pyramid of multiple image samples having different scaling factors, identify and aggregate features within one or more of the multiple image samples having different scaling factors, access the classification model, provide the features as input to the classification model, and receive an output indicative of objects detected within the image data.

Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices for autonomous vehicle operation are provided. For example, a computing system can receive object data that includes portions of sensor data. The computing system can determine, in a first stage of a multiple stage classification using hardware components, one or more first stage characteristics of the portions of sensor data based on a first machine-learned model. In a second stage of the multiple stage classification, the computing system can determine second stage characteristics of the portions of sensor data based on a second machine-learned model. The computing system can generate an object output based on the first stage characteristics and the second stage characteristics. The object output can include indications associated with detection of objects in the portions of sensor data.

Abstract: In one example embodiment, a computer-implemented method includes obtaining sensor data from a sensor, the sensor data corresponding to an image frame, and the sensor data including a first portion that corresponds to a portion of the image frame. The method includes pipelining the first portion of the sensor data into a machine-learned model before the sensor data corresponding to the entire image frame is transferred from the sensor to a memory device, to perform one or more inference operations on the first portion of the sensor data. The method includes generating as an output of the machine-learned model, in response to pipelining the sensor data corresponding to each portion of the image frame into the machine-learned model, a detection or classification of the one or more objects indicated within the sensor data.

Abstract: Image processing systems can include one or more cameras configured to obtain image data, one or more memory devices configured to store a classification model that classifies image features within the image data as including or not including detected objects, and a field programmable gate array (FPGA) device coupled to the one or more cameras. The FPGA device is configured to implement one or more image processing pipelines for image transformation and object detection. The one or more image processing pipelines can generate a multi-scale image pyramid of multiple image samples having different scaling factors, identify and aggregate features within one or more of the multiple image samples having different scaling factors, access the classification model, provide the features as input to the classification model, and receive an output indicative of objects detected within the image data.

Abstract: Systems, methods, tangible non-transitory computer-readable media, and devices for autonomous vehicle operation are provided. For example, a computing system can receive object data that includes portions of sensor data. The computing system can determine, in a first stage of a multiple stage classification using hardware components, one or more first stage characteristics of the portions of sensor data based on a first machine-learned model. In a second stage of the multiple stage classification, the computing system can determine second stage characteristics of the portions of sensor data based on a second machine-learned model. The computing system can generate an object output based on the first stage characteristics and the second stage characteristics. The object output can include indications associated with detection of objects in the portions of sensor data.

Abstract: Image processing systems can include one or more cameras configured to obtain image data, one or more memory devices configured to store a classification model that classifies image features within the image data as including or not including detected objects, and a field programmable gate array (FPGA) device coupled to the one or more cameras. The FPGA device is configured to implement one or more image processing pipelines for image transformation and object detection. The one or more image processing pipelines can generate a multi-scale image pyramid of multiple image samples having different scaling factors, identify and aggregate features within one or more of the multiple image samples having different scaling factors, access the classification model, provide the features as input to the classification model, and receive an output indicative of objects detected within the image data.

Abstract: One or more techniques and/or systems are provided for multicast transport configuration, for multicast transport, and/or for fault policy implementation. In an example, a multicast component may receive a data copy request from an application to copy data to multiple destinations. A scheduler component may create a transport schedule specifying an order with which to facilitate data copy operations across transports, such as heterogeneous transports, to the destinations. A dispatcher component may apply application specified transport modifiers to the data copy operations (e.g., a modification to a quality of service for a transport). The dispatcher component may facilitate the data copy operations and provide operation result information to a policy agent. The policy agent may provide notifications of data copy operation statuses from the operation result information and/or may implement a fault policy (e.g., a retry on a different transport) for a data copy operation that experienced a fault.

Abstract: One or more techniques and/or systems are provided for multicast transport configuration, for multicast transport, and/or for fault policy implementation. In an example, a multicast component may receive a data copy request from an application to copy data to multiple destinations. A scheduler component may create a transport schedule specifying an order with which to facilitate data copy operations across transports, such as heterogeneous transports, to the destinations. A dispatcher component may apply application specified transport modifiers to the data copy operations (e.g., a modification to a quality of service for a transport). The dispatcher component may facilitate the data copy operations and provide operation result information to a policy agent. The policy agent may provide notifications of data copy operation statuses from the operation result information and/or may implement a fault policy (e.g., a retry on a different transport) for a data copy operation that experienced a fault.

Abstract: One or more techniques and/or systems are provided for multicast transport configuration, for multicast transport, and/or for fault policy implementation. In an example, a multicast component may receive a data copy request from an application to copy data to multiple destinations. A scheduler component may create a transport schedule specifying an order with which to facilitate data copy operations across transports, such as heterogeneous transports, to the destinations. A dispatcher component may apply application specified transport modifiers to the data copy operations (e.g., a modification to a quality of service for a transport). The dispatcher component may facilitate the data copy operations and provide operation result information to a policy agent. The policy agent may provide notifications of data copy operation statuses from the operation result information and/or may implement a fault policy (e.g., a retry on a different transport) for a data copy operation that experienced a fault.

Abstract: In an aspect of the subject matter, a “full” amount of the flash cache (e.g., storage cells) is initially utilized to store data i.e., substantially all of the storage space of the flash cache may be designated to store user data, with the remaining storage space designated to store ECC information (e.g., parity bits) associated with a predefined ECC algorithm utilized to encode the user data. When a bit errors associated with the user data reaches a predefined threshold value, the storage space of the flash cache may transition to store less user data so as to accommodate the space needed to store ECC information associated with a stronger ECC algorithm. The storage space of the flash cache designated to store user data is reduced, while the storage space designated to store ECC information is increased to accommodate the stronger ECC algorithm.

Abstract: In an aspect of the subject matter, a “full” amount of the flash cache (e.g., storage cells) is initially utilized to store data i.e., substantially all of the storage space of the flash cache may be designated to store user data, with the remaining storage space designated to store ECC information (e.g., parity bits) associated with a predefined ECC algorithm utilized to encode the user data. When a bit errors associated with the user data reaches a predefined threshold value, the storage space of the flash cache may transition to store less user data so as to accommodate the space needed to store ECC information associated with a stronger ECC algorithm. The storage space of the flash cache designated to store user data is reduced, while the storage space designated to store ECC information is increased to accommodate the stronger ECC algorithm.

Abstract: One or more techniques and/or systems are provided for multicast transport configuration, for multicast transport, and/or for fault policy implementation. In an example, a multicast component may receive a data copy request from an application to copy data to multiple destinations. A scheduler component may create a transport schedule specifying an order with which to facilitate data copy operations across transports, such as heterogeneous transports, to the destinations. A dispatcher component may apply application specified transport modifiers to the data copy operations (e.g., a modification to a quality of service for a transport). The dispatcher component may facilitate the data copy operations and provide operation result information to a policy agent. The policy agent may provide notifications of data copy operation statuses from the operation result information and/or may implement a fault policy (e.g., a retry on a different transport) for a data copy operation that experienced a fault.

Abstract: One or more techniques and/or systems are provided for multicast transport configuration, for multicast transport, and/or for fault policy implementation. In an example, a multicast component may receive a data copy request from an application to copy data to multiple destinations. A scheduler component may create a transport schedule specifying an order with which to facilitate data copy operations across transports, such as heterogeneous transports, to the destinations. A dispatcher component may apply application specified transport modifiers to the data copy operations (e.g., a modification to a quality of service for a transport). The dispatcher component may facilitate the data copy operations and provide operation result information to a policy agent. The policy agent may provide notifications of data copy operation statuses from the operation result information and/or may implement a fault policy (e.g., a retry on a different transport) for a data copy operation that experienced a fault.

Abstract: One or more techniques and/or systems are provided for multicast transport configuration, for multicast transport, and/or for fault policy implementation. In an example, a multicast component may receive a data copy request from an application to copy data to multiple destinations. A scheduler component may create a transport schedule specifying an order with which to facilitate data copy operations across transports, such as heterogeneous transports, to the destinations. A dispatcher component may apply application specified transport modifiers to the data copy operations (e.g., a modification to a quality of service for a transport). The dispatcher component may facilitate the data copy operations and provide operation result information to a policy agent. The policy agent may provide notifications of data copy operation statuses from the operation result information and/or may implement a fault policy (e.g., a retry on a different transport) for a data copy operation that experienced a fault.

Abstract: In an aspect of the subject matter, a “full” amount of the flash cache (e.g., storage cells) is initially utilized to store data i.e., substantially all of the storage space of the flash cache may be designated to store user data, with the remaining storage space designated to store ECC information (e.g., parity bits) associated with a predefined ECC algorithm utilized to encode the user data. When a bit errors associated with the user data reaches a predefined threshold value, the storage space of the flash cache may transition to store less user data so as to accommodate the space needed to store ECC information associated with a stronger ECC algorithm. The storage space of the flash cache designated to store user data is reduced, while the storage space designated to store ECC information is increased to accommodate the stronger ECC algorithm.