Systems and Methods for Segregating Machine Learned Models for Distributed Processing

Systems and methods of the present disclosure are directed to a method for editing a machine-learned model to facilitate distributed processing. The method can include obtaining a machine-learned graph model comprising a plurality of connected nodes. The method can include determining a plurality of processing capabilities for a respective plurality of computation resources. The method can include determining a plurality of portions from the machine-learned graph model, wherein each of the plurality of portions comprises a respective subset of the plurality of nodes and a minimum processing capability. The method can include assigning each of the plurality of portions to a respective computation resource of the plurality of computation resources based at least in part on the minimum processing capability of a respective portion and the processing capability of the respective computation resource.

FIELD

The present disclosure relates generally to efficient processing of machine learned models, and, more particularly, segregation of machine learned models for optimized distributed processing.

BACKGROUND

An autonomous vehicle can be capable of sensing its environment and navigating without human input. In particular, an autonomous vehicle can observe its surrounding environment using a variety of sensors and can attempt to comprehend the environment by performing various processing techniques on data collected by the sensors. Given knowledge of its surrounding environment, the autonomous vehicle can identify an appropriate motion path for navigating through such a surrounding environment

SUMMARY

One example aspect of the present disclosure is directed to a computer-implemented method for editing a machine-learned model to facilitate distributed processing. The method can include obtaining, by a computing system comprising one or more computing devices, a machine-learned graph model comprising a plurality of connected nodes. The method can include determining, by the computing system, a plurality of processing capabilities for a respective plurality of computation resources. The method can include determining, by the computing system, a plurality of portions from the machine-learned graph model, wherein each of the plurality of portions comprises a respective subset of the plurality of nodes and a minimum processing capability. The method can include assigning, by the computing system, each of the plurality of portions to a respective computation resource of the plurality of computation resources based at least in part on the minimum processing capability of a respective portion and the processing capability of the respective computation resource.

Another aspect of the present disclosure is directed to a computing system of an autonomous vehicle. The computing system can include a plurality of computation resources comprising a respective plurality of processing capabilities. The computing system can include one or more tangible, non-transitory computer readable media storing computer-readable instructions that when executed by at least one of the plurality of computation resources cause the at least one of the plurality of computation resources to perform operations. The operations can include obtaining an optimized machine-learned graph model comprising a plurality of portions and portion assignment data descriptive of an assignment of each of the plurality of portions to a respective computation resource of the plurality of computation resources, wherein the plurality of portions are associated with a respective plurality of minimum processing capabilities, wherein the portion assignment data is based at least in part on the plurality of processing capabilities and the plurality of minimum processing capabilities. The operations can include obtaining input data from one or more systems of the autonomous vehicle, wherein the input data is associated with an autonomous vehicle processing task. The operations can include processing the input data with the optimized machine-learned graph model based at least in part on the portion assignment data to obtain output data associated with the autonomous vehicle processing task. The operations can include providing the output data to one or more additional systems of the autonomous vehicle.

Another example aspect of the present disclosure is directed to one or more tangible, non-transitory computer readable media storing computer-readable instructions that when executed by one or more processors cause the one or more processors to perform operations. The operations can include obtaining a graph model comprising a plurality of connected nodes. The operations can include determining a plurality of processing capabilities for a respective plurality of computation resources. The operations can include determining a plurality of portions from the graph model, wherein each of the plurality of portions comprises a respective subset of the plurality of nodes and a minimum processing capability. The operations can include assigning each of the plurality of portions to a respective computation resource of the plurality of computation resources based at least in part on the minimum processing capability of a respective portion and the processing capability of the respective computation resource. The operations can include providing each of the plurality of portions to a respective computation resource to which the portion is assigned, wherein the respective computation resource is configured to process the respective portion.

Other example aspects of the present disclosure are directed to other systems, methods, apparatuses, and the like for machine learning optimization, model-hardware deployment, and autonomous systems operations.

The machine learning technology described herein can help improve the efficiency of computing systems and reduce compute times. Moreover, the machine learning technology of the present disclosure can allow for systems utilizing it (e.g. autonomous vehicle computing systems) to make more calculations and thus, provide more data within a certain time interval compared to a system without. For example, in some embodiments, an autonomous vehicle computing system would be able to complete predictive calculations regarding driving operations (e.g. left turn, right turn, accelerate, brake, etc.) in less time than current embodiments and provide for a more accurate and safer operation.

DETAILED DESCRIPTION

Example aspects of the present application are directed to distributed processing of machine-learned models. More particularly, the systems and methods of the present disclosure provide a method to segregate a machine-learned graph model into portions (e.g., portions of the model), and provide these portions to distributed computation resources (e.g., physical and/or virtualized CPU(s), ASIC(s), FPGA(s), etc.). As an example, a machine-learned graph model (e.g., a graph-based neural network, etc.) can be obtained that includes a plurality of connected nodes (e.g., neuron(s) of a neural network, neural network(s), etc.). The machine-learned graph model can be segregated into various portions, which can each require a minimum processing capability (e.g., a minimum floating point precision, computation bandwidth, etc.). After segregating these portions of the model, a processing capability can be determined for a plurality of computation resources (e.g., discrete processor(s) onboard an autonomous vehicle computing system, etc.). Based on the required minimum processing capability for the portions, and the processing capability of the computation resources, each of the portions can be assigned to a respective computation resources for future processing, and in some implementations can be further optimized for their assigned computation resource. In such fashion, systems and methods of the present disclosure can be utilized to segregate and assign portions of a machine-learned model to hardware resources of various capabilities to optimally facilitate distributed processing of the machine-learned model.

More particularly, a computing system can obtain a graph model. As an example, the computing system can be a computing system associated with a service entity (e.g., a facilitator of autonomous vehicle services, etc.). The graph model can be, for example, a machine-learned graph model. A machine-learned graph model can be a machine-learned graph neural network that can be utilized to perform at least a portion of the computations required to facilitate operation of an autonomous vehicle (e.g., perception operation(s), prediction operation(s), motion planning operation(s), etc.). The graph model can be one that is ultimately implemented on an autonomous vehicle for performing the autonomy functions of the autonomous vehicle. It should be noted that each of the connected nodes of the graph model can include one or more neural unit(s) (e.g., neuron(s), layer(s), portion(s), etc.) of a neural network, and/or can include one or more neural network(s). Additionally, in some implementations, the portion(s) can include deterministic operation(s) and/or algorithm(s) that are not necessarily machine-learned (e.g., pre-processing operation(s), mathematical operation(s), post-processing algorithm(s), etc.).

It should be noted that although the present disclosure is described primarily in the context of facilitating autonomous vehicle operations for an autonomous vehicle computing system, aspects of the present disclosure are not limited to this context. Rather, systems and methods of the present disclosure can provide segregation of a machine-learned graph model that is trained for any purpose. As an example, systems and methods of the present disclosure can be used to segregate a machine-learned graph model for backend operations across a system of distributed processing devices (e.g., cloud-based image processing, route planning, service optimization, vehicle distribution, statistical analysis, etc.). As another example, systems and methods of the present disclosure can be used to segregate a graph that includes a plurality of machine-learned models across a plurality of computing systems (e.g., assignment of a plurality of machine-learned models that are components of a directed graph based processing architecture). In such fashion, systems and methods of the present disclosure can be applied in a broad variety of machine-learning applications to more facilitate segregation, assignment, and optimization of machine-learned models to various computation resources for distributed processing.

To help facilitate the model-hardware segregation, the computing system can identify the available hardware resources of a computing system (e.g., the same computing system, a vehicle computing system, etc.). For instance, the computing system can determine a plurality of processing capabilities for a respective plurality of computation resources. A computation resource can include at least a portion of any physical or virtual computation devices (e.g., processors, processor cores, memory devices, application-specific integrated circuits, graphics processing units, tensor processing unit(s), field-programmable gate arrays, accelerators, etc.). Each of the computation resources can include and/or be associated with a processing capability (e.g., a floating point precision, a processing throughput, an output accuracy, a processing latency, etc.). As an example, a vehicle computing system can include a processor with a plurality of processing cores (e.g., sixteen processing cores, etc.), a large graphics processing unit (GPU), a small GPU, and a field-programmable gate array (FPGA). A first computational resource may include the large GPU, and the large GPU can be capable of performing processing operations at a very high accuracy (e.g., a floating point precision of 32 bits (FP32), a relatively large FP32 processing throughput, etc.), therefore providing a high level of processing capability. A second computational resource may include the FPGA, and the FPGA can be capable of performing processing operations at an accuracy and/or speed less than the GPU (e.g., a precision of 8 bits (INT32), a relatively small INT8 processing throughput, etc.), therefore providing the second computation resource with a processing capability less than the first computation resource. As such, the physical and/or virtual processing devices of computation resources can vary, and the processing capabilities of the computation resources can vary accordingly.

In some implementations, the processing capability of a computation resource can be based at least in part on the location of a computation resource within a computing system. As an example, a computation resource can be located closer to certain resources of the computing system (e.g., certain level(s) of memory, etc.) can have a higher processing capability than a computation resource located further from the resources. As such, the positioning and/or access to certain portions of a computing system can at least partially determine the processing capability of a computation resource.

In some implementations, a computation resource can include a portion of a physical or virtual processing device. As an example, a first computation resource can be or otherwise include the tensor cores of a graphics processing unit, and a second computation resource can be or otherwise include the compute unified device architecture (CUDA) cores of a graphics processing unit. As another example, a first computation resource can be or otherwise include virtualized processor core(s) executed by a core of a physical CPU while a second computation resource can be or otherwise include additional virtualized processor cores(s) provided by the processor core and/or additional processor core(s) of the CPU. As such, a computation resource can include any portion and/or number of physical and/or virtualized processing device(s).

To help better allocate the graph model to the various computation resources, the computing system can separate the graph model into a plurality of portions. For instance, a plurality of portions can be determined from the machine-learned graph model. More particularly, the machine-learned graph model can be segregated (e.g., “cut”) at certain points to determine a plurality of portions of the machine-learned graph model. As an example, the machine-learned graph model can be or otherwise include a machine-learned graph neural network that includes a plurality of connected nodes. Subsets of the nodes can be cut away from the graph neural network to form portions of the network. For example, the machine-learned graph neural network can include 5 nodes, and a first portion can include the first two nodes and a portion can include the last three nodes. It should be noted that the segregation of node subsets from the machine-learned graph model can be performed using any conventional machine-learning tools and/or libraries (e.g., a deep learning library with graph network support, etc.).

Each of the portions of the machine-learned graph model can be associated with a minimum processing capability. The minimum processing capability can be a level of processing capability for a computation resource required to process the portion. In some implementations, the minimum processing capability can be described using the same metrics as the processing capability of the computation resources (e.g., a floating point precision, a processing throughput, a processing latency, etc.). In some implementations, the minimum processing capability can be described using different metrics than the processing capability of the computation resources. The computing system can be configured to translate the metric(s) associated with at least one of the minimum processing capability and/or the processing capability of the computation resources in order to help compare/match these elements.

In some implementations, the plurality of nodes of the machine-learned graph model can each be connected to at least one other node of the plurality of nodes. Further, in some implementations, each of the plurality of nodes can include one or more neural units of a neural network. As an example, a node of the machine-learned graph model may include one or more neurons (e.g., a single neuron, a layer of neurons from the graph model, a plurality of layers of neurons, etc.). As another example, a node of the machine-learned graph model may include one or more machine-learned functions (e.g., an activation layer, etc.). As yet another example, a node of the machine-learned graph model may be configured to perform one or more algorithms on received data (e.g., image preprocessing, etc.).

In some implementations, the subset of nodes of a portion of the machine-learned graph model may include nodes of differing minimum processing capabilities. As an example, a first node of the subset of nodes may have a minimum processing capability including a floating point precision of 16 bits. A second node of the subset of nodes may have a minimum processing capability including a floating point precision of 32 bits. In some implementations, the minimum processing capability of the portion itself can be that of the highest individual node of its subset of nodes. To follow the previous example, the portion including the first node and the second node could have a minimum processing capability including a floating point precision of 32 bits. Alternatively, in some implementations, each node of a subset of nodes of a portion of the machine-learned graph model can have identical minimum processing capabilities.

In some implementations, the segmentation of the model to form the portions (e.g., the subsets of nodes) can be based on the minimum accuracy and/or speed required to perform the operations of the subsets of nodes. As an example, if each of the operations in a subset of nodes requires a floating point precision of FP32, the subset of nodes can be “cut” to be included in a portion so that the entire subset of nodes can be processed by a computation resource capable of processing at a precision of FP32. As another example, a first subset of nodes can have an associated processing latency. A second subset of nodes can be “cut” to be included in a second portion such that a third portion of nodes can receive the outputs of the first and second portions at an optimal time. More particularly, the subset of nodes can be included in the portion to minimize and/or optimize the latency of the distributed processing of the machine-learned graph model.

Based on the minimum processing capabilities of the portions and the processing capabilities of the computation resources, each of the plurality of portions can be assigned to a respective computation resource of the plurality of computation resources. As an example, a first portion can have a minimum processing capacity that includes a minimum floating point precision of 32 bits, while a second portion can have a minimum floating point precision of 16 bits. A first computation resource can have a processing capability that includes a maximum floating point precision of 16 bits. A second computation resource can have a processing capability that includes a maximum floating point precision of 32 bits. The first portion can be assigned to the second computation resource and the second portion can be assigned to the first computation resource. In such fashion, each of the computation resources can be utilized to an optimal degree to process portions of the machine-learned graph model.

In some implementations, one or more connections can be generated between each of the portions to obtain a reconstructed machine-learned graph model. More particularly, the connections between nodes of the machine-learned graph model (e.g., the connections between the subsets of the nodes) can be restored to reconstruct the machine-learned graph model for processing. After obtaining the reconstructed machine-learned graph model, the model can be provided to the plurality of computation resources. The plurality of computation resources can be configured to process the reconstructed machine-learned graph model (e.g., according to the assignment of the portions to the each of the computation resources, etc.).

Alternatively, or additionally, in some implementations, each of the plurality of machine-learned portions can be provided to a respective computation resource to which the machine-learned portion is assigned. Each of the computation resources can be configured to process an assigned portion of the machine-learned graph model. In such fashion, the segregated machine-learned graph model (e.g., the plurality of portions) can be provided to the plurality of hardware resources for optimal distributed processing.

In some implementations, the computing system can obtain data descriptive of a processing performance of each of the computation resources. The processing performance can describe one or more processing metrics (e.g., a processing latency, a processing bandwidth, an accuracy of output data associated with the processing, etc.). As an example, the data descriptive of the processing performance may indicate that the output data from processing a portion by a computation resource was not accurate enough to facilitate proper operations (e.g., of an autonomous vehicle task, etc.). As another example, the data descriptive of the processing performance may indicate that the output data from processing of a portion by a computation resource can be less accurate and still facilitate proper operations. As such, the data descriptive of the processing performances can be utilized to further optimize the structure of the portions and/or the assignment of portions to computation resources.

In some implementations, one or more optimizations can be applied to one or more respective portions to obtain an optimized machine-learned graph model. The one or more optimizations can be based at least in part on the processing performance of the one or more computation resources assigned to process the one or more portions of the machine-learned graph model. The optimization(s) can include adjusting the minimum processing capability of the portion(s) and/or compressing a subset of nodes of the portion(s). As an example, a processing performance of a first computation resource assigned to a first portion may indicate that an output of the computation resource can be less accurate and can still facilitate proper operations. In response, the first portion can be optimized. For example, the subset of nodes of the first portion can be compressed (e.g., compressing layer(s) of a neural network, combining the operations of two nodes into one node, etc.). For another example, the minimum processing capability of the first portion can be reduced (e.g., reduced from a 32-bit floating point minimum precision to a 16-bit floating point minimum precision, etc.).

In some implementations, the one or more optimizations can be further based at least in part on an autonomous vehicle processing task respectively associated with the machine-learned portion. More particularly, the optimizations can be based on the proposed autonomous vehicle task output assigned to the portion of the machine-learned graph model. As an example, a portion may be associated with a data processing task and/or data preprocessing task (e.g., LIDAR data preprocessing and/or processing, image data preprocessing and/or processing, etc.). As another example, a portion may be associated with a task (e.g., image segmentation, LIDAR segmentation, fused segmentation, detection, estimation, etc.). As yet another example, a portion may be associated with one or more mathematical operations. It should be noted that, in some implementations, the optimization(s) can occur either before or after the graph model is reconnected.

The optimized graph model can be implemented onboard an autonomous vehicle. For example, an onboard computing system of an autonomous vehicle (e.g., an autonomous vehicle computing system, etc.) can include a plurality of computation resources that include a respective plurality of processing capabilities. The computation resources and associated processing capabilities can be the same or substantially similar to those described previously. The computing system can obtain an optimized machine-learned graph model (e.g., optimized according to the systems and methods of the present disclosure, etc.). For example, the graph model may be accessed, received, retrieved, downloaded, stored, etc. onto the autonomous vehicle computing system (e.g., from an offboard repository, etc.). The graph model can include the plurality of portions, and can further include and/or be obtained in association with portion assignment data. The portion assignment data can describe an assignment of each of the portions to a respective computation resource.

The portion assignment data can be based at least in part on the plurality of processing capabilities of the computation resources and a minimum processing capability for each of the portions of the model. More particularly, the assignment data can include assignments of the portions to various computation resources such that the portions are assigned to computation resources that are sufficiently capable of processing the assigned portions. As an example, the portion assignment data can describe an assignment of a first portion with a minimum processing capability including a floating point precision of 32 bits to a computation resource with a processing capability including a floating point precision of 32 bits.

The computing system of the autonomous vehicle can obtain input data from one or more systems of the autonomous vehicle (e.g., a perception system, a prediction system, a motion planning system, a remote computing system, an operations computing system, a sensor system, a secondary vehicle computing system, an autonomy computing system, etc.). The input data can be associated with an autonomous vehicle task. As an example, the input data can be associated with a perception task (e.g., a task to perceive an environment about the autonomous vehicle, etc.). As another example, the input data can be associated with a prediction task (e.g., a task to predict future event(s) regarding the environment or one or more objects about the autonomous vehicle, etc.). As yet another example, the input data can be associated with a motion planning task (e.g., a task to plan the motion of the vehicle in response to perceived and/or predicted event(s) regarding the environment or one or more objects about the autonomous vehicle, etc.).

The computing system of the autonomous vehicle can process the input data with the optimized machine-learned graph model based at least in part on the portion assignment data to obtain output data. The output data can be associated with the autonomous vehicle processing task. As an example, the autonomous vehicle processing task can be a perception task and the output data can be perception data (e.g., identification of object(s) in an environment about the autonomous vehicle, etc.). As another example, the autonomous vehicle processing task can be a prediction task and the output data can be prediction data (e.g., predicted movement of the one or more objects perceived in the environment about the autonomous vehicle, etc.). As yet another example, the autonomous vehicle processing task can be a motion planning task and the output data can be motion planning data (e.g., data to control motion of the autonomous vehicle in response to the predicted movement of object(s) perceived to be in the environment about the autonomous vehicle, etc.).

In some implementations, the computing system can process the input data with the optimized machine-learned graph model by processing each portion of the model with the computation resource that is assigned to each portion by the portion assignment data. As an example, a first portion can be assigned to a first computation resource and a second portion can be assigned to a second computation resource. The first portion can be processed with the first computation resource and the second portion can be processed by the second computation resource.

The output data can be provided to one or more additional systems of the autonomous vehicle. As an example, the motion planning data can be provided to a vehicle control system (e.g., and/or an associated intermediate interface, etc.) of the autonomous vehicle to control the autonomous vehicle in accordance with the motion planning data. The various system(s) of the autonomous vehicle will be discussed in greater detail with regards toFIG. 1.

In some implementations, data descriptive of a processing performance of the computation resources can be provided to a computing system associated with a service entity. Further, the autonomous vehicle processing task can be associated with a service provided and/or facilitated by the service entity. As an example, the service entity can facilitate an autonomous vehicle service (e.g., a transportation service, delivery service, rideshare service, etc.). The autonomous vehicle task can be associated with the autonomous vehicle service (e.g., a motion planning task for operating the autonomous vehicle to fulfill the autonomous vehicle service). The computing system to which the data is provided can be associated with the service entity. For example, the computing system may be a computing system utilized by the service entity to optimize the optimized machine-learned graph model and to generate the portion assignment data (e.g., according to the methods of the present disclosure, etc.).

In some implementations, in response to providing the data descriptive of the processing performance, the computing system of the autonomous vehicle can obtain an updated machine-learned graph model different from the first optimized model and/or updated portion assignment data different than the portion assignment data. As an example, the provided data may indicate a poor processing performance associated with the processing of a first portion by a first computation resource. In response, the updated assignment data can reassign the first portion to a different computation resource. Alternatively, or additionally, the updated machine-learned graph model may include further optimizations to the optimized model. To follow the previous example, the updated machine-learned graph model may include an increased minimum processing capability for the first portion and/or may have compressed the first portion (e.g., consolidated one or more neural network layers, reassigned one or more neural units from the first portion to a different portion, etc.).

Various means can be configured to perform the methods and processes described herein. For example, a computing system can include graph model obtaining unit(s), processing capability determination unit(s), portion determination unit(s), portion assigning unit(s), and/or other means for performing the operations and functions described herein. In some implementations, one or more of the units may be implemented separately. In some implementations, one or more units may be a part of or included in one or more other units. These means can include processor(s), microprocessor(s), graphics processing unit(s), logic circuit(s), dedicated circuit(s), application-specific integrated circuit(s), programmable array logic, field-programmable gate array(s), controller(s), microcontroller(s), and/or other suitable hardware. The means can also, or alternately, include software control means implemented with a processor or logic circuitry, for example. The means can include or otherwise be able to access memory such as, for example, one or more non-transitory computer-readable storage media, such as random-access memory, read-only memory, electrically erasable programmable read-only memory, erasable programmable read-only memory, flash/other memory device(s), data registrar(s), database(s), and/or other suitable hardware.

The means can be programmed to perform one or more algorithm(s) for carrying out the operations and functions described herein. For instance, the means can be configured to obtain a graph model (e.g., a machine-learned graph neural network, etc.). A graph model obtaining unit is an example of means for obtaining a machine-learned graph model as described herein.

The means can be configured to determine a processing capability for a plurality of a respective plurality of computation resources. For example, the means can be configured to determine that each of a plurality of computation resources has a certain processing capability. A processing capability determination unit is one example of a means for determining a processing capability for a plurality of a respective plurality of computation resources as described herein.

The means can be configured to determine a plurality of portions from a machine-learned graph model. For example, the means can be configured to segregate a machine-learned graph neural network into subsets of network nodes. A portion determination unit is one example of a means for determining a plurality of portions from a machine-learned graph model as described herein.

The means can be configured to assign a plurality of portions of a machine-learned graph model to a plurality of computation resources. For example, the means can be configured to assign each of the plurality of machine-learned portions to a respective computation resource of the plurality of computation resources. A portion assigning unit is one example of a means for assigning a plurality of portions of a machine-learned graph model to a plurality of computation resources as described herein.

The present disclosure provides a number of technical effects and benefits. As one example technical effect and benefit, the systems and methods of the present disclosure enable more optimal usage of computation resources in a computing system (e.g., various physical and/or virtual processing resources of an autonomous vehicle computing system, etc.). As an example, computation resources are often under-utilized due to a lack of perceived accuracy when processing machine-learned models. For example, a relatively accurate computation resource (e.g., a graphics processing unit) may perform most or all of the processing of a machine-learned graph model while a relatively inaccurate computation resource (e.g., an FPGA) would inefficiently sit idle. By segregating a machine-learned model into portions, and determining a minimum processing capability for each portion, various portions can be distributed to each of the computation resources in the computing system, therefore significantly increasing utilization of all computation resources in a system and decreasing inefficiencies such as unused computation resources sitting idle. In turn, this increase in resource utilization can lead to lower hardware costs, reduced processing latency, and increased processing accuracy, which in the context of autonomous vehicles can be utilized to further ensure the absolute safety of passengers.

With reference now to the FIGS., example aspects of the present disclosure will be discussed in further detail.FIG. 1depicts a block diagram of an example system100for controlling and communicating with a vehicle according to example aspects of the present disclosure. As illustrated,FIG. 1shows a system100that can include a vehicle105and a vehicle computing system110associated with the vehicle105. The vehicle computing system100can be located onboard the vehicle105(e.g., it can be included on and/or within the vehicle105).

The vehicle105incorporating the vehicle computing system100can be various types of vehicles. For instance, the vehicle105can be an autonomous vehicle. The vehicle105can be a ground-based autonomous vehicle (e.g., car, truck, bus, etc.). The vehicle105can be an air-based autonomous vehicle (e.g., airplane, helicopter, vertical take-off and lift (VTOL) aircraft, etc.). The vehicle105can be a light weight elective vehicle (e.g., bicycle, scooter, etc.). The vehicle105can be another type of vehicles (e.g., watercraft, etc.). The vehicle105can drive, navigate, operate, etc. with minimal and/or no interaction from a human operator (e.g., driver, pilot, etc.). In some implementations, a human operator can be omitted from the vehicle105(and/or also omitted from remote control of the vehicle105). In some implementations, a human operator can be included in the vehicle105.

The vehicle105can be configured to operate in a plurality of operating modes. The vehicle105can be configured to operate in a fully autonomous (e.g., self-driving) operating mode in which the vehicle105is controllable without user input (e.g., can drive and navigate with no input from a human operator present in the vehicle105and/or remote from the vehicle105). The vehicle105can operate in a semi-autonomous operating mode in which the vehicle105can operate with some input from a human operator present in the vehicle105(and/or a human operator that is remote from the vehicle105). The vehicle105can enter into a manual operating mode in which the vehicle105is fully controllable by a human operator (e.g., human driver, pilot, etc.) and can be prohibited and/or disabled (e.g., temporary, permanently, etc.) from performing autonomous navigation (e.g., autonomous driving, flying, etc.). The vehicle105can be configured to operate in other modes such as, for example, park and/or sleep modes (e.g., for use between tasks/actions such as waiting to provide a vehicle service, recharging, etc.). In some implementations, the vehicle105can implement vehicle operating assistance technology (e.g., collision mitigation system, power assist steering, etc.), for example, to help assist the human operator of the vehicle105(e.g., while in a manual mode, etc.).

To help maintain and switch between operating modes, the vehicle computing system110can store data indicative of the operating modes of the vehicle105in a memory onboard the vehicle105. For example, the operating modes can be defined by an operating mode data structure (e.g., rule, list, table, etc.) that indicates one or more operating parameters for the vehicle105, while in the particular operating mode. For example, an operating mode data structure can indicate that the vehicle105is to autonomously plan its motion when in the fully autonomous operating mode. The vehicle computing system110can access the memory when implementing an operating mode.

The operating mode of the vehicle105can be adjusted in a variety of manners. For example, the operating mode of the vehicle105can be selected remotely, off-board the vehicle105. For example, a remote computing system (e.g., of a vehicle provider and/or service entity associated with the vehicle105) can communicate data to the vehicle105instructing the vehicle105to enter into, exit from, maintain, etc. an operating mode. By way of example, such data can instruct the vehicle105to enter into the fully autonomous operating mode.

In some implementations, the operating mode of the vehicle105can be set onboard and/or near the vehicle105. For example, the vehicle computing system110can automatically determine when and where the vehicle105is to enter, change, maintain, etc. a particular operating mode (e.g., without user input). Additionally, or alternatively, the operating mode of the vehicle105can be manually selected via one or more interfaces located onboard the vehicle105(e.g., key switch, button, etc.) and/or associated with a computing device proximate to the vehicle105(e.g., a tablet operated by authorized personnel located near the vehicle105). In some implementations, the operating mode of the vehicle105can be adjusted by manipulating a series of interfaces in a particular order to cause the vehicle105to enter into a particular operating mode.

The vehicle computing system110can include one or more computing devices located onboard the vehicle105. For example, the computing device(s) can be located on and/or within the vehicle105. It should be noted that the computing devices located onboard the vehicle105can be or otherwise be included in computation resources of the vehicle computing system110. As an example, a computation resource of the vehicle computing system can include one or more computing devices. In some implementations, a computing device of the vehicle computing system110can be included in multiple computation resources. As an example, a first portion of a computing device (e.g., the tensor cores of a graphics processing unit, a core of a multicore CPU, etc.) can be or otherwise be included in a first computation resource. A second portion of the computing device (e.g., the CUDA cores of a graphics processing unit, a virtualized CPU or CPU core, etc.) can be included in a second computation resource. As such, the vehicle computing system110can include a plurality of computation resources, each computation resource including at least a portion of one or more computation devices (e.g., a portion of a GPU, multiple GPUs, a GPU and a portion of a CPU, etc.).

The computing device(s) and/or computation resources of the vehicle computing system can include various components for performing various operations and functions. As an example, the computing device(s) can include one or more application-specific integrated circuit(s), memory(s), processor(s), processor core(s), field-programmable gate array(s), accelerator(s), tensor processing unit(s), graphics processing unit(s), and/or any other computing devices to facilitate the operation of the aforementioned device(s) (e.g., a printed circuit board, motherboard, power supply, etc.). In some implementations, the vehicle computing system110can be partitioned into a plurality of independent, asymmetrically-powerful computation resources that each include one or more computation devices. Each of the computation resources can be configured to fully process the operations of a task at varying accuracies and/or speeds due to the difference in computing device(s) included in the various computation resources. As an example, a first computation resource can obtain data associated with a first task and generate an associated task output faster than a second computation resource due to an increased number of graphics processing units included in the first computation resource. As such, two computation resources of the vehicle computing system110can include varying amounts or types of computing device(s) and/or portion(s) of computing device(s).

Each of the computing device(s), and therefore each of the computation resources, of the vehicle computing system110can have or be associated with a certain processing capability (e.g., a floating point precision, a processing throughput, a processing latency, etc.). As an example, the vehicle computing system110can include a processor with a plurality of processing cores (e.g., sixteen processing cores, etc.), a large graphics processing unit (GPU), a small GPU, and a field-programmable gate array (FPGA). A first computational resource may include the large GPU and a corresponding processing capability provided by the relatively high accuracy and throughput of a GPU (e.g., a floating point precision of 32 bits (FP32), a relatively large FP32 processing throughput, etc.). A second computational resource may include the FPGA a corresponding processing capability provided by the relatively low accuracy and throughput of the FPGA (e.g., a precision of 8 bits (INT8), a relatively small INT8 processing throughput, etc.). As such, the physical and/or virtual processing devices of a computation resources can vary, and the processing capabilities of the computation resources can vary accordingly.

The computing device(s) and/or computation resources can include one or more processors and one or more tangible, non-transitory, computer readable media (e.g., memory devices, etc.). The one or more tangible, non-transitory, computer readable media can store instructions that when executed by the one or more processors cause the vehicle105(e.g., its computing system, one or more processors, etc.) to perform operations and functions, such as those described herein for segregation, distribution, and optimization of a machine-learned model (e.g., a graph model, etc.) across a number of computation resources included in the vehicle105(e.g., the one or more computing devices included in the vehicle computing system110, etc.).

The vehicle105can include a communications system115configured to allow the vehicle computing system110(and its computing device(s)) to communicate with other computing devices. The communications system115can include any suitable components for interfacing with one or more network(s)120, including, for example, transmitters, receivers, ports, controllers, antennas, and/or other suitable components that can help facilitate communication. In some implementations, the communications system115can include a plurality of components (e.g., antennas, transmitters, and/or receivers) that allow it to implement and utilize multiple-input, multiple-output (MIMO) technology and communication techniques.

The vehicle computing system110can use the communications system115to communicate with one or more computing device(s) that are remote from the vehicle105over one or more networks120(e.g., via one or more wireless signal connections). The network(s)120can exchange (send or receive) signals (e.g., electronic signals), data (e.g., data from a computing device), and/or other information and include any combination of various wired (e.g., twisted pair cable) and/or wireless communication mechanisms (e.g., cellular, wireless, satellite, microwave, and radio frequency) and/or any desired network topology (or topologies). For example, the network(s)120can include a local area network (e.g. intranet), wide area network (e.g. Internet), wireless LAN network (e.g., via Wi-Fi), cellular network, a SATCOM network, VHF network, a HF network, a WiMAX based network, and/or any other suitable communication network (or combination thereof) for transmitting data to and/or from the vehicle105and/or among computing systems.

More particularly, the vehicle computing system110may send data to and/or receive data from an operations computing system190A via network(s) (e.g., using communication system115, etc.). As an example, the operations computing system190A can send a graph model (e.g., an optimized machine-learned graph model, etc.) including a plurality of portions to the vehicle computing system110alongside portion assignment data. The portion assignment data can describe an assignment of each of the plurality of portions to a respective computation resource of the vehicle computing system110. The vehicle computing system110can also receive input data that is associated with an autonomous vehicle processing task (e.g., perception data175A for a perception task170A, prediction data175B for a prediction task170B, motion planning data175C for a motion planning task170C, etc.). The vehicle computing system110can process the input data using the portions of the graph model according to the portion assignment data to obtain output data that is associated with the autonomous vehicle processing task.

As an example, the graph model can be a graph model configured to process motion planning data175C to output a motion plan. Portions of the graph model can be assigned to each of the computation resources of the vehicle computing system110based on the portion assignment data received from the operations computing system190A. For example, a first portion of the model associated with input pre-processing operations can be assigned to a first computation resource and a second portion associated with other operations can be assigned to a second computation resource. The vehicle computing system110can process the motion planning data175C with the distributed portions and computation resources to obtain the motion plan output. The motion plan can be provided to one or more additional systems of the vehicle105(e.g., vehicle interface145, vehicle control system150, etc.).

In some implementations, the vehicle computing system110can evaluate the processing performance of each of the computation resources and provide data descriptive of the processing performance to a computing system. As an example, the vehicle computing system110can provide the data descriptive of the processing performance to the operations computing system190A via network(s)120using communication system115. After providing the data, the vehicle computing system110can obtain an updated machine-learned graph model from the operations computing system190A. The updated machine-learned graph model can be different than the optimized machine-learned graph model (e.g., updated portion assignment(s), compressed or altered portion(s), etc.). In such fashion, the operations computing system190A can work in concert with the vehicle computing system110of the vehicle105to optimally distribute machine-learned models across the variety of computing devices and/or computation resources of the vehicle computing system110.

In some implementations, the communications system115can also be configured to enable the vehicle105to communicate with and/or provide and/or receive data and/or signals from a remote computing device associated with a user125and/or an item (e.g., an item to be picked-up for a courier service). For example, the communications system115can allow the vehicle105to locate and/or exchange communications with a user device130of a user125. In some implementations, the communications system115can allow communication among one or more of the system(s) on-board the vehicle105.

As shown inFIG. 1, the vehicle105can include one or more sensors135, an autonomy computing system140, a vehicle interface145, one or more vehicle control systems150, and other systems, as described herein. One or more of these systems can be configured to communicate with one another via one or more communication channels. The communication channel(s) can include one or more data buses (e.g., controller area network (CAN)), on-board diagnostics connector (e.g., OBD-II), and/or a combination of wired and/or wireless communication links. The onboard systems can send and/or receive data, messages, signals, etc. amongst one another via the communication channel(s).

The sensor(s)135can be configured to acquire sensor data155. The sensor(s)135can be external sensors configured to acquire external sensor data. This can include sensor data associated with the surrounding environment of the vehicle105. The surrounding environment of the vehicle105can include/be represented in the field of view of the sensor(s)135. For instance, the sensor(s)135can acquire image and/or other data of the environment outside of the vehicle105and within a range and/or field of view of one or more of the sensor(s)135. The sensor(s)135can include one or more Light Detection and Ranging (LIDAR) systems, one or more Radio Detection and Ranging (RADAR) systems, one or more cameras (e.g., visible spectrum cameras, infrared cameras, etc.), one or more motion sensors, one or more audio sensors (e.g., microphones, etc.), and/or other types of imaging capture devices and/or sensors. The one or more sensors can be located on various parts of the vehicle105including a front side, rear side, left side, right side, top, and/or bottom of the vehicle105. The sensor data155can include image data (e.g., 2D camera data, video data, etc.), RADAR data, LIDAR data (e.g.,3D point cloud data, etc.), audio data, and/or other types of data. The vehicle105can also include other sensors configured to acquire data associated with the vehicle105. For example, the vehicle105can include inertial measurement unit(s), wheel odometry devices, and/or other sensors.

In some implementations, the sensor(s)135can include one or more internal sensors. The internal sensor(s) can be configured to acquire sensor data155associated with the interior of the vehicle105. For example, the internal sensor(s) can include one or more cameras, one or more infrared sensors, one or more motion sensors, one or more weight sensors (e.g., in a seat, in a trunk, etc.), and/or other types of sensors. The sensor data155acquired via the internal sensor(s) can include, for example, image data indicative of a position of a passenger or item located within the interior (e.g., cabin, trunk, etc.) of the vehicle105. This information can be used, for example, to ensure the safety of the passenger, to prevent an item from being left by a passenger, confirm the cleanliness of the vehicle105, remotely assist a passenger, etc.

In some implementations, the sensor data155can be indicative of one or more objects within the surrounding environment of the vehicle105. The object(s) can include, for example, vehicles, pedestrians, bicycles, and/or other objects. The object(s) can be located in front of, to the rear of, to the side of, above, below the vehicle105, etc. The sensor data155can be indicative of locations associated with the object(s) within the surrounding environment of the vehicle105at one or more times. The object(s) can be static objects (e.g., not in motion) and/or dynamic objects/actors (e.g., in motion or likely to be in motion) in the vehicle's environment. The sensor(s)135can provide the sensor data155to the autonomy computing system140.

In addition to the sensor data155, the autonomy computing system140can obtain map data160. The map data160can provide detailed information about the surrounding environment of the vehicle105and/or the geographic area in which the vehicle was, is, and/or will be located. For example, the map data160can provide information regarding: the identity and location of different roadways, road segments, buildings, or other items or objects (e.g., lampposts, crosswalks and/or curb); the location and directions of traffic lanes (e.g., the location and direction of a parking lane, a turning lane, a bicycle lane, or other lanes within a particular roadway or other travel way and/or one or more boundary markings associated therewith); traffic control data (e.g., the location and instructions of signage, traffic lights, and/or other traffic control devices); obstruction information (e.g., temporary or permanent blockages, etc.); event data (e.g., road closures/traffic rule alterations due to parades, concerts, sporting events, etc.); nominal vehicle path data (e.g., indicate of an ideal vehicle path such as along the center of a certain lane, etc.); and/or any other map data that provides information that assists the vehicle computing system110in processing, analyzing, and perceiving its surrounding environment and its relationship thereto. In some implementations, the map data160can include high definition map data. In some implementations, the map data160can include sparse map data indicative of a limited number of environmental features (e.g., lane boundaries, etc.). In some implementations, the map data can be limited to geographic area(s) and/or operating domains in which the vehicle105(or autonomous vehicles generally) may travel (e.g., due to legal/regulatory constraints, autonomy capabilities, and/or other factors).

The vehicle105can include a positioning system165. The positioning system165can determine a current position of the vehicle105. This can help the vehicle105localize itself within its environment. The positioning system165can be any device or circuitry for analyzing the position of the vehicle105. For example, the positioning system165can determine position by using one or more of inertial sensors (e.g., inertial measurement unit(s), etc.), a satellite positioning system, based on IP address, by using triangulation and/or proximity to network access points or other network components (e.g., cellular towers, WiFi access points, etc.) and/or other suitable techniques. The position of the vehicle105can be used by various systems of the vehicle computing system110and/or provided to a remote computing system. For example, the map data160can provide the vehicle105relative positions of the elements of a surrounding environment of the vehicle105. The vehicle105can identify its position within the surrounding environment (e.g., across six axes, etc.) based at least in part on the map data160. For example, the vehicle computing system110can process the sensor data155(e.g., LIDAR data, camera data, etc.) to match it to a map of the surrounding environment to get an understanding of the vehicle's position within that environment. Data indicative of the vehicle's position can be stored, communicated to, and/or otherwise obtained by the autonomy computing system140.

The autonomy computing system140can perform various functions for autonomously operating the vehicle105. For example, the autonomy computing system140can perform the following functions: perception170A, prediction170B, and motion planning170C. For example, the autonomy computing system130can obtain the sensor data155via the sensor(s)135, process the sensor data155(and/or other data) to perceive its surrounding environment, predict the motion of objects within the surrounding environment, and generate an appropriate motion plan through such surrounding environment. In some implementations, these autonomy functions can be performed by one or more sub-systems such as, for example, a perception system, a prediction system, a motion planning system, and/or other systems that cooperate to perceive the surrounding environment of the vehicle105and determine a motion plan for controlling the motion of the vehicle105accordingly. In some implementations, one or more of the perception, prediction, and/or motion planning functions170A,170B,170C can be performed by (and/or combined into) the same system and/or via shared computation resources. In some implementations, one or more of these functions can be performed via difference sub-systems. As further described herein, the autonomy computing system140can communicate with the one or more vehicle control systems150to operate the vehicle105according to the motion plan (e.g., via the vehicle interface145, etc.).

The vehicle computing system110(e.g., the autonomy computing system140) can identify one or more objects that within the surrounding environment of the vehicle105based at least in part on the sensor data135and/or the map data160. The objects perceived within the surrounding environment can be those within the field of view of the sensor(s)135and/or predicted to be occluded from the sensor(s)135. This can include object(s) not in motion or not predicted to move (static objects) and/or object(s) in motion or predicted to be in motion (dynamic objects/actors). The vehicle computing system110(e.g., performing the perception function170C, using a perception system, etc.) can process the sensor data155, the map data160, etc. to obtain perception data175A. The vehicle computing system110can generate perception data175A that is indicative of one or more states (e.g., current and/or past state(s)) of one or more objects that are within a surrounding environment of the vehicle105. For example, the perception data175A for each object can describe (e.g., for a given time, time period) an estimate of the object's: current and/or past location (also referred to as position); current and/or past speed/velocity; current and/or past acceleration; current and/or past heading; current and/or past orientation; size/footprint (e.g., as represented by a bounding shape, object highlighting, etc.); class (e.g., pedestrian class vs. vehicle class vs. bicycle class, etc.), the uncertainties associated therewith, and/or other state information. The vehicle computing system110can utilize one or more algorithms and/or machine-learned model(s) that are configured to identify object(s) based at least in part on the sensor data155. This can include, for example, one or more neural networks trained to identify object(s) within the surrounding environment of the vehicle105and the state data associated therewith. The perception data175A can be utilized for the prediction function175B of the autonomy computing system140.

The vehicle computing system110can be configured to predict a motion of the object(s) within the surrounding environment of the vehicle105. For instance, the vehicle computing system110can generate prediction data175B associated with such object(s). The prediction data175B can be indicative of one or more predicted future locations of each respective object. For example, the prediction system175B can determine a predicted motion trajectory along which a respective object is predicted to travel over time. A predicted motion trajectory can be indicative of a path that the object is predicted to traverse and an associated timing with which the object is predicted to travel along the path. The predicted path can include and/or be made up of a plurality of way points. In some implementations, the prediction data175B can be indicative of the speed and/or acceleration at which the respective object is predicted to travel along its associated predicted motion trajectory. The vehicle computing system110can utilize one or more algorithms and/or machine-learned model(s) that are configured to predict the future motion of object(s) based at least in part on the sensor data155, the perception data175A, map data160, and/or other data. This can include, for example, one or more neural networks trained to predict the motion of the object(s) within the surrounding environment of the vehicle105based at least in part on the past and/or current state(s) of those objects as well as the environment in which the objects are located (e.g., the lane boundary in which it is travelling, etc.). The prediction data175B can be utilized for the motion planning function170C of the autonomy computing system140.

The vehicle computing system110can determine a motion plan for the vehicle105based at least in part on the perception data175A, the prediction data175B, and/or other data. For example, the vehicle computing system110can generate motion planning data175C indicative of a motion plan. The motion plan can include vehicle actions (e.g., speed(s), acceleration(s), other actions, etc.) with respect to one or more of the objects within the surrounding environment of the vehicle105as well as the objects' predicted movements. The motion plan can include one or more vehicle motion trajectories that indicate a path for the vehicle105to follow. A vehicle motion trajectory can be of a certain length and/or time range. A vehicle motion trajectory can be defined by one or more way points (with associated coordinates). The planned vehicle motion trajectories can indicate the path the vehicle105is to follow as it traverses a route from one location to another. Thus, the vehicle computing system110can take into account a route/route data when performing the motion planning function170C.

The vehicle motion planning system can include an optimization algorithm, machine-learned model, etc. that considers cost data associated with a vehicle action as well as other objective functions (e.g., cost functions based on speed limits, traffic lights, etc.), if any, to determine optimized variables that make up the motion plan. The vehicle computing system110can determine that the vehicle105can perform a certain action (e.g., pass an object, etc.) without increasing the potential risk to the vehicle105and/or violating any traffic laws (e.g., speed limits, lane boundaries, signage, etc.). For instance, the vehicle computing system110can evaluate the predicted motion trajectories of one or more objects during its cost data analysis to help determine an optimized vehicle trajectory through the surrounding environment. The motion planning system180can generate cost data associated with such trajectories. In some implementations, one or more of the predicted motion trajectories and/or perceived objects may not ultimately change the motion of the vehicle105(e.g., due to an overriding factor). In some implementations, the motion plan may define the vehicle's motion such that the vehicle105avoids the object(s), reduces speed to give more leeway to one or more of the object(s), proceeds cautiously, performs a stopping action, passes an object, queues behind/in front of an object, etc.

The vehicle computing system110can be configured to continuously update the vehicle's motion plan and a corresponding planned vehicle motion trajectory. For example, in some implementations, the vehicle computing system110can generate new motion planning data175C/motion plan(s) for the vehicle105(e.g., multiple times per second, etc.). Each new motion plan can describe a motion of the vehicle105over the next planning period (e.g., next several seconds, etc.). Moreover, a new motion plan may include a new planned vehicle motion trajectory. Thus, in some implementations, the vehicle computing system110can continuously operate to revise or otherwise generate a short-term motion plan based on the currently available data. Once the optimization planner has identified the optimal motion plan (or some other iterative break occurs), the optimal motion plan (and the planned motion trajectory) can be selected and executed by the vehicle105.

The vehicle computing system110can cause the vehicle105to initiate a motion control in accordance with at least a portion of the motion planning data175C. A motion control can be an operation, action, etc. that is associated with controlling the motion of the vehicle105. For instance, the motion planning data175C can be provided to the vehicle control system(s)150of the vehicle105. The vehicle control system(s)150can be associated with a vehicle interface145that is configured to implement a motion plan. The vehicle interface145can serve as an interface/conduit between the autonomy computing system140and the vehicle control systems150of the vehicle105and any electrical/mechanical controllers associated therewith. The vehicle interface145can, for example, translate a motion plan into instructions for the appropriate vehicle control component (e.g., acceleration control, brake control, steering control, etc.). By way of example, the vehicle interface145can translate a determined motion plan into instructions to adjust the steering of the vehicle105“X” degrees, apply a certain magnitude of braking force, increase/decrease speed, etc. The vehicle interface145can help facilitate the responsible vehicle control (e.g., braking control system, steering control system, acceleration control system, etc.) to execute the instructions and implement a motion plan (e.g., by sending control signal(s), making the translated plan available, etc.). This can allow the vehicle105to autonomously travel within the vehicle's surrounding environment.

It should be noted that any and/or all of the processing tasks associated with perception task170A, prediction task170B, and motion planning task170C can be performed using the machine-learned model(s) of the present disclosure (e.g., the machine-learned graph model, the machine-learned optimized graph model, etc.). More particularly, the portions of the graph model can be associated with processing tasks of any of the tasks170A-170C of the autonomy computing system140. As an example, each portion of the graph model can be associated with various processing operations of the perception task170A. As another example, a first portion of the graph can be associated with perception task170, a second portion can be associated with prediction task170B, and third and fourth portions can be associated with motion planning task170C. As such, the machine-learned model(s) of the present embodiments, and the portions of said models, can be utilized to process any or all tasks of the vehicle computing system110and/or the autonomy computing system140.

The vehicle computing system110can store other types of data. For example, an indication, record, and/or other data indicative of the state of the vehicle (e.g., its location, motion trajectory, health information, etc.), the state of one or more users (e.g., passengers, operators, etc.) of the vehicle, and/or the state of an environment including one or more objects (e.g., the physical dimensions and/or appearance of the one or more objects, locations, predicted motion, etc.) can be stored locally in one or more memory devices of the vehicle105. Additionally, the vehicle105can communicate data indicative of the state of the vehicle, the state of one or more passengers of the vehicle, data descriptive of a processing performance of the computation resources of the vehicle computing system110, and/or the state of an environment to a computing system that is remote from the vehicle105, which can store such information in one or more memories remote from the vehicle105. Moreover, the vehicle105can provide any of the data created and/or store onboard the vehicle105to another vehicle.

The vehicle computing system110can include the one or more vehicle user devices180. For example, the vehicle computing system110can include one or more user devices with one or more display devices located onboard the vehicle105. A display device (e.g., screen of a tablet, laptop, and/or smartphone) can be viewable by a user of the vehicle105that is located in the front of the vehicle105(e.g., driver's seat, front passenger seat). Additionally, or alternatively, a display device can be viewable by a user of the vehicle105that is located in the rear of the vehicle105(e.g., a back passenger seat). The user device(s) associated with the display devices can be any type of user device such as, for example, a table, mobile phone, laptop, etc. The vehicle user device(s)180can be configured to function as human-machine interfaces. For example, the vehicle user device(s)180can be configured to obtain user input, which can then be utilized by the vehicle computing system110and/or another computing system (e.g., a remote computing system, etc.). For example, a user (e.g., a passenger for transportation service, a vehicle operator, etc.) of the vehicle105can provide user input to adjust a destination location of the vehicle105. The vehicle computing system110and/or another computing system can update the destination location of the vehicle105and the route associated therewith to reflect the change indicated by the user input.

The vehicle105can be configured to perform vehicle services for one or a plurality of different service entities185. A vehicle105can perform a vehicle service by, for example and as further described herein, travelling (e.g., traveling autonomously) to a location associated with a requested vehicle service, allowing user(s) and/or item(s) to board or otherwise enter the vehicle105, transporting the user(s) and/or item(s), allowing the user(s) and/or item(s) to deboard or otherwise exit the vehicle105, etc. In this way, the vehicle105can provide the vehicle service(s) for a service entity to a user.

A service entity185can be associated with the provision of one or more vehicle services. For example, a service entity can be an individual, a group of individuals, a company (e.g., a business entity, organization, etc.), a group of entities (e.g., affiliated companies), and/or another type of entity that offers and/or coordinates the provision of one or more vehicle services to one or more users. For example, a service entity can offer vehicle service(s) to users via one or more software applications (e.g., that are downloaded onto a user computing device), via a website, and/or via other types of interfaces that allow a user to request a vehicle service. As described herein, the vehicle services can include transportation services (e.g., by which a vehicle transports user(s) from one location to another), delivery services (e.g., by which a vehicle transports/delivers item(s) to a requested destination location), courier services (e.g., by which a vehicle retrieves item(s) from a requested origin location and transports/delivers the item to a requested destination location), and/or other types of services. The vehicle services can be wholly performed by the vehicle105(e.g., travelling from the user/item origin to the ultimate destination, etc.) or performed by one or more vehicles and/or modes of transportation (e.g., transferring the user/item at intermediate transfer points, etc.).

As described previously, the operations computing system190A of the service entity185can help to coordinate the performance of vehicle services by autonomous vehicles (e.g., by optimizing performance of distributed model processing by the vehicle computing system110, etc.). The operations computing system190A can include and/or implement one or more service platforms of the service entity. The operations computing system190A can include one or more computing devices. The computing device(s) can include various components for performing various operations and functions. For instance, the computing device(s) can include one or more processors and one or more tangible, non-transitory, computer readable media (e.g., memory devices, etc.). The one or more tangible, non-transitory, computer readable media can store instructions that when executed by the one or more processors cause the operations computing system190(e.g., its one or more processors, etc.) to perform operations and functions, such as those described herein matching users and vehicles/vehicle fleets, segregation of machine-learned model(s), assignment and optimization of portions of segregated machine-learned model(s), etc.

A user125can request a vehicle service from a service entity185. For example, the user125can provide user input to a user device130to request a vehicle service (e.g., via a user interface associated with a mobile software application of the service entity185running on the user device130). The user device130can communicate data indicative of a vehicle service request195to the operations computing system190A associated with the service entity185(and/or another associated computing system that can then communicate data to the operations computing system190A). The vehicle service request195can be associated with a user. The associated user can be the one that submits the vehicle service request (e.g., via an application on the user device130). In some implementations, the user may not be the user that submits the vehicle service request. The vehicle service request can be indicative of the user. For example, the vehicle service request can include an identifier associated with the user and/or the user's profile/account with the service entity185. The vehicle service request195can be generated in a manner that avoids the use of personally identifiable information and/or allows the user to control the types of information included in the vehicle service request195. The vehicle service request195can also be generated, communicated, stored, etc. in a secure manner to protect information.

The vehicle service request195can indicate various types of information. For example, the vehicle service request194can indicate the type of vehicle service that is desired (e.g., a transportation service, a delivery service, a courier service, etc.), one or more locations (e.g., an origin location, a destination location, etc.), timing constraints (e.g., pick-up time, drop-off time, deadlines, etc.), and/or geographic constraints (e.g., to stay within a certain area, etc.). The service request195can indicate a type/size/class of vehicle such as, for example, a sedan, an SUV, luxury vehicle, standard vehicle, etc. The service request195can indicate a product of the service entity185. For example, the service request195can indicate that the user is requesting a transportation pool product by which the user would potentially share the vehicle (and costs) with other users/items. In some implementations, the service request195can explicitly request for the vehicle service to be provided by an autonomous vehicle or a human-driven vehicle. In some implementations, the service request195can indicate a number of users that will be riding in the vehicle/utilizing the vehicle service. In some implementations, the service request195can indicate preferences/special accommodations of an associated user (e.g., music preferences, climate preferences, wheelchair accessibility, etc.) and/or other information.

The operations computing system190A of the service entity185can process the data indicative of the vehicle service request195and generate a vehicle service assignment that is associated with the vehicle service request. The operations computing system can identify one or more vehicles that may be able to perform the requested vehicle services to the user195. The operations computing system190A can identify which modes of transportation are available to a user for the requested vehicle service (e.g., light electric vehicles, human-drive vehicles, autonomous vehicles, aerial vehicle, etc.) and/or the number of transportation modes/legs of a potential itinerary of the user for completing the vehicle service (e.g., single or plurality of modes, single or plurality of legs, etc.). For example, the operations computing system190A can determined which autonomous vehicle(s) are online with the service entity185(e.g., available for a vehicle service assignment, addressing a vehicle service assignment, etc.) to help identify which autonomous vehicle(s) would be able to provide the vehicle service.

The operations computing system190A and/or the vehicle computing system110can communicate with one or more other computing systems190B that are remote from the vehicle105. This can include, for example, computing systems associated with government functions (e.g., emergency services, regulatory bodies, etc.), computing systems associated with vehicle providers other than the service entity, computing systems of other vehicles (e.g., other autonomous vehicles, aerial vehicles, etc.). Communication with the other computing systems190B can occur via the network(s)120.

FIG. 2depicts a diagram of an example computing system200including one or more of a plurality of computation resources (e.g., plurality of computation resources205A-N) of the system of the present disclosure (e.g., vehicle computing system110ofFIG. 1). The plurality of computation resources205A-N can include one or more computation resources configured to communicate over one or more wired and/or wireless communication channels (e.g., wired and/or wireless networks). Each computation resource (e.g.,205A) can be associated with a type, an operating system250, and/or one or more designated tasks. A type, for example, can include an indication of the one or more designated tasks of a respective computation resource205A. The one or more designated tasks, for example, can include performing one or more processes220A-N. As an example, each of the processes220A-220N can correspond to a portion of a machine-learned model, and as such each computation resource205A-N can perform operations of a process associated with a respective portion of the machine-learned model.

Each computation resource205A of the plurality of computation resources205A-N can include and/or have access to at least a portion of each of one or more processors255(e.g., graphics processing unit(s), FPGA(s), CPU(s), tensor processing unit(s), etc.) and/or one or more memories260(e.g., RAM memory, ROM memory, cache memory, flash memory, etc.). The one or more memories260can include one or more tangible non-transitory computer readable instructions that, when executed by the one or more processors255, cause the computation resource205A to perform one or more operations. The operations can include, for example, executing one or more of a plurality of processes of the computing system200. For instance, each computation resource205A can include a compute node configured to run one or more processes220A-N of the plurality of processes.

For example, the computation resource205A can include an orchestration service210. The orchestration service210can include a start-up process of the computation resource205A. The orchestration service210can, for example, include an operating system service (e.g., a service running as part of the operating system250). In addition, or alternatively, the orchestration service can include a gRPC service. The computation resource205A can run the orchestration service210to configure and start processes220A-220N of the computation resource205A. In some implementations, the orchestration service210can include a primary orchestrator and/or at least one of a plurality of secondary orchestrators. For example, each respective computation resource of the plurality of computation resources can include at least one of the plurality of secondary orchestrators. The primary orchestrator can be configured to receive global configuration data and provide the global configuration data to the plurality of secondary orchestrators. The global configuration data, for example, can include one or more instructions indicative of the one or more designated tasks for each respective computation resource(s)205A-N, a software version and/or environment on which to run a plurality of processes (e.g.,220A-220N of the computation resource205A) of the computing system200, etc. A secondary orchestrator for each respective computation resource can receive the global configuration data and configure and start one or more processes at the respective computation resource based on the global configuration data.

Additionally, the orchestration service210can perform segregation of a machine-learned model into portions, and can perform assignment of each of these portions to a computation resource205A-N (e.g., generate portion assignment data descriptive of the assignments, etc.). As an example, the orchestration service can analyze a machine-learned model (e.g., a graph neural network, etc.) and determine a plurality of portions from the machine-learned model (e.g., based on a minimum processing capability, etc.). Each of the portions can be associated with a process (e.g., processes220A-220N) that include the operations to process the portion of the machine-learned model. The orchestration service can subsequently assign each of the portions of the machine-learned model to a respective computation resource205A-N. In such fashion, each of the computation resources205A-N can be assigned a portion of the machine-learned model for processing (e.g., the process associated with the portion, etc.).

It should be noted that each portion of a machine-learned model does not need to be associated with a respective process (e.g., process220A-N). More particularly, a process (e.g., process220A-N) can perform operations associated with each portion of a machine-learned model. As an example, a model can be segregated into portions that are processed as a function graph225, which can be processed by one process or across a plurality of processes (e.g., process220A-N).

Each process (e.g., process220A,220B) can include a plurality of function nodes235(e.g., a machine-learned portion, components of a machine-learned portion, etc.) connected by one or more directed edges that dictate the flow of data between the plurality of function nodes235. Each computation resource205A can execute (e.g., via one or more processors, etc.) a respective plurality of function nodes235to run a respective process220A,220B. For example, the plurality of function nodes235can be arranged in one or more function graphs225. A function graph225can include a series of function nodes235arranged (e.g., by one or more directed edges) in a pipeline, graph architecture, etc.

For example, with reference toFIG. 3,FIG. 3depicts a diagram of an example functional graph225according to example implementations of the present disclosure. The function graph225can include a plurality of function nodes235A-F, one or more injector nodes230A-B, one or more ejector nodes240A-B, and/or one or more directed edges245.

Each of the function nodes235A-F can be associated with one or more portions of a segregated machine-learned model. For example, a function node235A can be associated with one or more first layers of a first portion of a machine-learned graph model, function node235B can be associated with one or more second layers of a second portion of the machine-learned graph model, injector node230A can be associated with one or more input layers of the machine-learned graph model, and ejector node240A can be associated with one or more output layers of a machine-learned graph model (e.g., an argmax layer, a softmax layer, other custom mathematical operations, etc.). As another example, each of the function nodes235A-F, injector nodes (e.g., input nodes)230A-B, and ejector nodes (e.g., output nodes)240A-B can be associated with one portion of a machine-learned graph model. As such, each of the nodes ofFIG. 3can correspond to any operations associated with one or more portions of a segregated machine-learned model.

Additionally, or alternatively, each of the function nodes235A-F can include computing function(s) associated with one or more inputs (e.g., of one or more data types) and one or more outputs (e.g., of one or more data types). For example, the function nodes235A-F can be implemented such that they define one or more accepted inputs and one or more outputs. In some implementations, each function node235A-F can be configured to obtain one or more inputs of a single data type, perform one or more functions on the one or more inputs, and output one or more outputs of a single data type.

The function nodes235A-F can be connected by one or more directed edges245of the function graph225(and/or a subgraph225A,225B of the function graph225with reference toFIG. 2). The one or more directed edges245can dictate how data flows through the function graph225(and/or the subgraphs225A,225B ofFIG. 2). For example, the one or more directed edges245can be formed based on the defined inputs and outputs of each of the function nodes235A-F of the function graph225. Each function graph225can include one or more injector nodes230A-B and one or more ejector nodes240A-B configured to communicate with one or more remote computation resources and/or processes (e.g., processes220C-220N ofFIG. 2) outside the function graph225. The injector nodes230A-B, for example, can be configured to communicate with one or more computation resources and/or processes (e.g., processes220C-220N ofFIG. 2) outside the function graph225to obtain input data for the function graph225. By way of example each of the one or more injector nodes230A-B can include a function configured to obtain and/or process sensor data from a respective sensor280shown inFIG. 2(e.g., sensor(s)135ofFIG. 1). The ejector nodes240A-B can be configured to communicate with one or more computation resources205B-N and/or processes220C-220N outside the function graph225to provide output data of the function graph225to the one or more computation resources205B-N and/or processes220C-220N.

Turning back toFIG. 2, each computation resource205A-N can be configured to execute one or more function graphs225to run one or more processes220A,220B of the plurality of processes220A-N of the respective computation resource205A. For example, as described herein, each respective computation resource can be configured to run a respective set of processes based on global configuration data (e.g., portion assignment data, etc.). Each process220A-N can include an executed instance of a function graph and/or a subgraph of a function graph. For example, in some implementations, a function graph225can be separated across multiple processes220A,220B. Each process220A,220B can include a subgraph225A,225B (e.g., process220A including subgraph225A, process220B including subgraph225B, etc.) of the function graph225. In such a case, each process220A,220B of the function graph225can be communicatively connected by one or more function nodes235of the function graph225. In this manner, each respective computation resource205A-N can be configured to run a respective process by executing a respective function graph and/or a subgraph of the respective function graph. Thus, each function graph can be implemented as a single process or multiple processes, and accordingly, each portion of a machine-learned model can be assigned to a respective computation resource of the plurality of computation resources205A-N.

The plurality of computation resources205A-N, sensors280, processes220A-N (e.g., of each respective computation resource205A), etc. can be communicatively connected over one or more wireless and/or wired networks270. For instance, the plurality of computation resources205A-N (and/or processes220A-N of computation resource205A) can communicate over one or more communication channels270. For example, process(es) at each computation resource can exchange messages over the one or more communicative channels270using a message interchange format (e.g., JSON, IDL, etc.). By way of example, each respective process can utilize one or more communication protocols (e.g., HTTP, REST, gRPC, etc.) to provide and/or receive messages from one or more respective computation resource processes (e.g., other processes running on the same computation resource) and/or remote processes (e.g., processes running on one or more other computation resources of the computing system).

Alternatively, or additionally, each of the computation resources205A-N, sensors280, processes220A-N, etc. can be components of a singular computing system (e.g., vehicle computing system110ofFIG. 1). As an example, the computation resources205A-N can be groupings of computing device(s) included in the vehicle computing system ofFIG. 1.

For example, the vehicle computing system110can include a plurality of processors255, each with varying processing capabilities (e.g., FPGAs, ASICs, CPUs, etc.). Computation resource205A can be or otherwise include a GPU and computation resource205B can be or otherwise include an FPGA. Each of computation resources205A and205B can be assigned one or more processes associated with a first and second portion of a machine-learned model. The computation resources205A can receive an input (e.g., at an injector node230A of an associated function sub-graph225A), and can process the input with the one or more processes associated with the first portion to generate an output (e.g., at an ejector node240A of the associated function graph225). The output can be communicated to the second computation resource205B via a communication system (e.g., a serial bus, a printed circuit board, a bridge, a network port, communication system115ofFIG. 1, etc.). The output can be received at the second resource205B (e.g., at an injector node230B of an associated function sub-graph225B) and can be processed with the one or more processes associated with the second portion of the model to generate a second output. In such fashion, the machine-learned model can be segregated according to embodiments of the present disclosure, and can be assigned via an orchestration service (e.g., orchestration service210) to various computation resources (e.g., resources205A-N) for processing.

FIG. 4depicts an example computing system400including a plurality of computation resources402and404according to example embodiments of the present disclosure. Each of the computation resource(s)402and404can be or otherwise be included in the computing systems described in the present disclosure (e.g., vehicle computing system110ofFIG. 1, etc.). It should be noted that the devices included in computation resources402/404(e.g., ASICs404A, GPUs416A/B, FPGAs412, etc.) are depicted as being included in computation resource(s) merely to illustrate example embodiment(s). Rather, each of the depicted devices can be a computation resource or can otherwise be included in computation resource(s). As an example, both or either of the ASICs404A could be a computation resource. As another example, the large GPU416A and the small GPU416B can be a computation resource together, or can be individual portions included in multiple computation resources. As such, any device of a computing system (e.g., vehicle computing system110, operations computing system190A, etc.) can be considered a computation resource, or can be included in one or more computation resources.

More particularly, computation resource(s)402can be or otherwise include a computation resource including a plurality of computation devices (e.g., ASICs404A, FPGAs404B, memories408, CPU406, etc.). Additionally, or alternatively, each of the computing devices included in computation resource(s)402can be or otherwise be included in a computation resource. More particularly, computing devices (e.g., FPGAs, CPUs, CPU cores, GPUs, etc.) can be dynamically partitioned as computation resource(s). As an example, the computation resource(s)402could be or otherwise include both of the ASICs404A. The computation resource(s)402could then be dynamically partitioned to further include one or more cores of the central processing unit406. Other resources (e.g., FPGAs404B and memories408, etc.) could be included in a different computation resource. Dynamic partitioning of various computing devices or portions of computing devices into a computation resource can be based on a variety of factors, including but not limited to available hardware resources, hardware failure(s), anticipated (e.g., predicted) processing operation(s), received data types and/or quantities, etc.

Similarly, computation resource(s)404can include a plurality of computing devices that can each be or otherwise be included in the computation resource(s)404and/or a different computation resource. As an example, the computation resource404can include both FPGAs412and one of the two CPUs414. Further, the computation resource404can include a small GPU416B, while the computation resource402includes a large GPU416A. In such fashion, a computation resource can include different sizes of computational devices. Following the previous example, a different computation resource could include the second CPU414and the memories410.

FIG. 5depicts a data flow diagram500for determining and assigning portions of a graph model to computation resources of a computing system according to example embodiments of the present disclosure. More particularly, a graph model501can include a plurality of various interconnected processing operations. As depicted, the model501can include sensor data1input502(e.g., LIDAR data, image data, ultrasonic data, heat data, radar data, etc.), sensor data2input504(e.g., LIDAR data, image data, ultrasonic data, heat data, radar data, etc.), sensor data1preprocessing506, math operations507, LIDAR pre-processing508, fused trunk510, and post-processing512. Each of these processing operations502-512can be performed by and/or associated with one or more layers and/or components (e.g., one or more neural network neurons, submodel(s), portion(s), etc.) of the graph model501. As an example, the sensor data1input502and sensor data2input504operations could be associated with one or more input layers of the graph model. As another example, the post-processing operations512could be associated with one or more output layers of the graph model (e.g., softmax layer(s), argmax layer(s), etc.). Further, each of these processing operations can be associated with one or more nodes (e.g., compute node(s)) of the graph model.

The graph model501can be processed by a computing system that includes a plurality of computation resources526-530(e.g., GPUs, ASICs, FPGAs, CPUs, etc.). As described previously, the processing of a non-segregated, conventional machine-learned model is generally performed by a single computational resource (e.g., a GPU, etc.), leaving other computation resources idle. To help better allocate the model (e.g., graph model501) to the various computation resources, a computing system can perform portion determination514to separate the graph model501into a plurality of portions516,518,520, and522. As described previously, the graph model501can be or otherwise include a plurality of connected nodes that correspond to the processing operations depicted for graph model501(e.g.,502-512). Subsets of these nodes can be cut away from the graph model501to form portions of the model516-522.

More particularly, node(s) associated with sensor data1input502, sensor data1preprocessing506, and math ops507can be segregated from the graph model501and included in a first portion516. Node(s) associated with sensor data2input504and sensor data2pre-processing508can be segregated from the graph model501and included in a second portion518. Node(s) associated with fused trunk510can be segregated from the graph model501and included in a third portion520. Node(s) associated with post-processing/output512can be segregated from the graph model501and included in a fourth portion522.

The segregation of these portions516-522of the graph model501can be associated with a minimum processing capability. The minimum processing capability can be a level of processing capability for a computation resource (e.g.,526-532) required to process the operations included in the portion, and the minimum processing capability can be determined based on the requirements of the operations. The minimum processing capability can be described using different metrics than the processing capability of the computation resources. The computing system can be configured to translate the metric(s) associated with at least one of the minimum processing capability and/or the processing capability of the computation resources in order to help compare/match these elements.

As an example, portion516can be determined to have a minimum processing capability of FP32 due to one or more of the operations of portion516(e.g., sensor data1input502, sensor data1preprocessing506, math ops507, etc.) requiring an accuracy of FP64. As another example, portion2518can be determined to have a minimum processing capability of FP16 due both sensor data2input504and sensor data2pre-processing508operations requiring a minimum of at most FP16. In such fashion, portion520can be determined to have a minimum processing capability of FP32 and portion522can be determined to have a minimum processing capability of FP16.

It should be noted that, in some implementations, the segmentation of the graph model501to form the portions516-522can be based on the minimum accuracy and/or speed required to perform the operations of the portions. As an example, if each of the operations in portion516(e.g., sensor data1input502, sensor data1preprocessing506, math ops507, etc.) requires a floating point precision of FP64, the subset of nodes associated with operations502,506, and507can be “cut” to be included in the portion516so that the entire subset of nodes can be processed by a computation resource capable of processing at a precision of FP32 (e.g., computation resource526, computation resource530, etc.).

The computation resources (e.g.,526,528,530,532) of the computing system can include and/or be associated with a processing capability. This processing capability can be determined based on the device(s) or portion(s) of device(s) included in each computational resource (e.g.,526,528,530,532). More particularly, each of the computation resources (e.g.,526,528,530,532) can include at least a portion of any physical or virtual computation devices (e.g., processors, processor cores, memory devices, application-specific integrated circuits, graphics processing units, tensor processing unit(s), field-programmable gate arrays, accelerators, etc.). Each of the computation resources (e.g.,526,528,530,532) can include and/or be associated with a processing capability (e.g., a floating point precision, a processing throughput, a processing latency, etc.). As an example, a vehicle computing system can include a processor with a plurality of processing cores (e.g., sixteen processing cores, etc.), a large graphics processing unit (GPU), a small GPU, and a field-programmable gate array (FPGA). A first computational resource526may include the large GPU, and the large GPU can be capable of performing processing operations at a very high accuracy (e.g., a floating point precision of 32 bits (FP32), a relatively large FP32 processing throughput, etc.), therefore providing the first computation resource526a high level of processing capability. A second computational resource528may include an FPGA, and the FPGA can be capable of performing processing operations at an accuracy and/or speed less than the GPU of computation resource526(e.g., a floating point precision of 16 bits (FP16), a relatively small FP16 processing throughput, etc.), therefore providing the second computation resource528with a processing capability less than the first computation resource526. As such, the physical and/or virtual processing devices of computation resources can vary, and the processing capabilities of the computation resources can vary accordingly (e.g.,526,528,530,532).

Based on the minimum processing capabilities of the portions (e.g.,516,518,520,522), and the processing capabilities of the computation resources (e.g.,526,528,530,532), each of the plurality of portions516-522can be assigned to a respective computation resource of the plurality of computation resources526-530via resource assignment524. As an example, first portion516has a minimum processing capability of FP32, and portion2518has as minimum processing capability of FP16. First portion516can be assigned to computing resource526, which has a processing capability of FP32 and therefore meets the minimum requirement of first portion516. Similarly, second portion518can be assigned to computing resource528, which has a processing capability of FP16 and therefore meets the minimum requirement of second portion518. Accordingly, third portion520can be assigned to computing resource530and fourth portion522, which has minimum processing capability of INT8, can be assigned to computing resource532, which has a processing capability of INT8. In such fashion, during resource assignment524each of the portions516-522can respectively be assigned to a computation resource526-532that fulfills the minimum processing requirement of the portion.

It should be noted that although floating point precision (e.g., FP32, FP16, INT8, etc.) is depicted as a representation of processing capability, precision is merely one metric among a multitude of metrics to evaluate processing capability. Other metrics, such as bandwidth, throughput, processing latency, access to certain specialized resources (e.g., tensor cores, etc.), and any other performance-based processing metric can be utilized to establish a processing capability and a minimum processing capability. As such, processing capability can be a measure of any performance metric that is relevant to the workload being processed (e.g., speed, bandwidth, accuracy, etc.).

After assigning the portions516-522to the respective computation resources526-532, the assignments can be saved as portion assignment data534. The portion assignment data534can describe each of the assignments of the plurality of portions (e.g.,516,518,520,522) to a respective computation resource (e.g.,526,528,530,532, etc.). It should be noted that portion determination514and resource assignment524can be performed by a computing device and/or system that is distinct from the computing system that includes the plurality of computation resources526-532.

Using the computing systems ofFIG. 1as an example, the computation resources526-532can be included in the vehicle computing system110ofFIG. 1. The operations computing system190A ofFIG. 1can perform portion determination514and resource assignment524to determine the plurality of portions516-522and generate the portion assignment data534. The portion assignment data and the portions516-522can be provided by the operations computing system190A to the vehicle computing system110(e.g., via network(s)120using communication system115). The vehicle computing system can process each of the provided graph model portions516-522using the respectively assigned portion (e.g.,526,528,530,532, etc.) that is described by the portion assignment data534.

In such fashion, an operations computing system (e.g., system190A, etc.) associated with a service entity (e.g., entity185) can obtain a machine-learned model (e.g., graph model501), segregate the model into a plurality of portions (e.g.,516-522), determine a minimum processing capability for the portions and a processing capability for the computation resources (e.g.,526-532) of a vehicle computing system (e.g., system110). The operations computing system can provide the assignments (e.g., portion assignment data534) to the vehicle computing system alongside the portions, therefore optimizing the vehicle computing system by reducing the number of idle computational resources of the vehicle computing system.

FIG. 6Adepicts a flow diagram of an example method600A for determining and assigning portions of a machine-learned model to computation resources of a computing system according to example embodiments of the present disclosure. One or more portion(s) of the method600A can be implemented by one or more computing devices such as, for example, the computing devices described inFIGS. 1, 2, 4, and 5. Moreover, one or more portion(s) of the method600A can be implemented as an algorithm on the hardware components of the device(s) described herein (e.g., as inFIGS. 1, 2, 4, 7, and 8) to, for example, segregate and assign portions of a machine-learned model.FIG. 6Adepicts elements performed in a particular order for purposes of illustration and discussion. Those of ordinary skill in the art, using the disclosures provided herein, will understand that the elements of any of the methods discussed herein can be adapted, rearranged, expanded, omitted, combined, and/or modified in various ways without deviating from the scope of the present disclosure.

At (602), the method600A can include obtaining a machine-learned graph model. For instance, a computing system (e.g., operations computing system190A) can obtain a graph model (e.g., a machine-learned graph neural network, etc.). The operations computing system can be a computing system associated with a service entity (e.g., service entity185ofFIG. 1, etc.). The graph model can be, for example, a machine-learned graph model. A machine-learned graph model can be a machine-learned graph neural network that can be utilized to perform at least a portion of the computations required to facilitate operation of an autonomous vehicle (e.g., perception operation(s), prediction operation(s), motion planning operation(s), etc.). The graph model can be one that is ultimately implemented on an autonomous vehicle for performing the autonomy functions of the autonomous vehicle. It should be noted that each of the connected nodes of the graph model can include one or more neural unit(s) (e.g., neuron(s), layer(s), portion(s), etc.) of a neural network, and/or can include one or more neural network(s). Additionally, in some implementations, the portion(s) can include deterministic operation(s) and/or algorithm(s) that are not necessarily machine-learned (e.g., pre-processing operation(s), mathematical operation(s), post-processing algorithm(s), etc.).

It should be noted that although the present disclosure is described primarily in the context of facilitating autonomous vehicle operations for an autonomous vehicle computing system, aspects of the present disclosure are not limited to this context. Rather, systems and methods of the present disclosure can provide segregation of a machine-learned graph model that is trained for any purpose. As an example, systems and methods of the present disclosure can be used to segregate a machine-learned graph model for backend operations across a system of distributed processing devices (e.g., cloud-based image processing, route planning, service optimization, vehicle distribution, statistical analysis, etc.). As another example, systems and methods of the present disclosure can be used to segregate a graph that includes a plurality of machine-learned models across a plurality of computing systems (e.g., assignment of a plurality of machine-learned models that are components of a directed graph based processing architecture). In such fashion, systems and methods of the present disclosure can be applied in a broad variety of machine-learning applications to more facilitate segregation, assignment, and optimization of machine-learned models to various computation resources for distributed processing.

At (604), the method600A can include determining a plurality of processing capabilities for a plurality of computation resources. For instance, a computing system (e.g., operations computing system190A), to help facilitate model-hardware segregation, can identify the available hardware resources of a computing system (e.g., the same operations computing system190A, the vehicle computing system110, etc.). For instance, the computing system can determine a plurality of processing capabilities for a respective plurality of computation resources. A computation resource can include at least a portion of any physical or virtual computation devices (e.g., processors, processor cores, memory devices, application-specific integrated circuits, graphics processing units, tensor processing unit(s), field-programmable gate arrays, accelerators, etc.). Each of the computation resources can include and/or be associated with a processing capability (e.g., a floating point precision, a processing throughput, a processing latency, etc.). As an example, a vehicle computing system can include a processor with a plurality of processing cores (e.g., sixteen processing cores, etc.), a large graphics processing unit (GPU), a small GPU, and a field-programmable gate array (FPGA). A first computational resource may include the large GPU, and the large GPU can be capable of performing processing operations at a very high accuracy (e.g., a floating point precision of 64 bits (FP64), a relatively large FP64 processing throughput, etc.), therefore providing a high level of processing capability. A second computational resource may include the FPGA, and the FPGA can be capable of performing processing operations at an accuracy and/or speed less than the GPU (e.g., a floating point precision of 32 bits (FP32), a relatively small FP32 processing throughput, etc.), therefore providing the second computation resource with a processing capability less than the first computation resource. As such, the physical and/or virtual processing devices of computation resources can vary, and the processing capabilities of the computation resources can vary accordingly.

In some implementations, a computation resource can include a portion of a physical or virtual processing device. As an example, a first computation resource can be or otherwise include the tensor cores of a graphics processing unit, and a second computation resource can be or otherwise include the compute unified device architecture (CUDA) cores of a graphics processing unit. As another example, a first computation resource can be or otherwise include virtualized processor core(s) executed by a core of a physical CPU while a second computation resource can be or otherwise include additional virtualized processor cores(s) provided by the processor core and/or additional processor core(s) of the CPU. As such, a computation resource can include any portion and/or number of physical and/or virtualized processing device(s).

At (606), the method600A can include determining a plurality of machine-learned portions from the graph model. For instance, to help better allocate the graph model to the various computation resources a computing system (e.g., operations computing system190A) can separate the graph model into a plurality of portions. For instance, a plurality of portions can be determined from the machine-learned graph model. More particularly, the machine-learned graph model can be segregated (e.g., “cut”) at certain points to determine a plurality of portions of the machine-learned graph model. As an example, the machine-learned graph model can be or otherwise include a machine-learned graph neural network that includes a plurality of connected nodes. Subsets of the nodes can be cut away from the graph neural network to form portions of the network. For example, the machine-learned graph neural network can include 5 nodes, and a first portion can include the first two nodes and a portion can include the last three nodes. It should be noted that the segregation of node subsets from the machine-learned graph model can be performed using any conventional machine-learning tools and/or libraries (e.g., PyTorch, etc.).

Each of the portions of the machine-learned graph model can be associated with a minimum processing capability. The minimum processing capability can be a level of processing capability for a computation resource required to process the portion. In some implementations, the minimum processing capability can be described using the same metrics as the processing capability of the computation resources (e.g., a floating point precision, a processing throughput, a processing latency, etc.). In some implementations, the minimum processing capability can be described using different metrics than the processing capability of the computation resources. The computing system can be configured to translate the metric(s) associated with at least one of the minimum processing capability and/or the processing capability of the computation resources in order to help compare/match these elements.

In some implementations, the plurality of nodes of the machine-learned graph model can each be connected to at least one other node of the plurality of nodes. Further, in some implementations, each of the plurality of nodes can include one or more neural units of a neural network. As an example, a node of the machine-learned graph model may include one or more neurons (e.g., a single neuron, a layer of neurons from the graph model, a plurality of layers of neurons, etc.). As another example, a node of the machine-learned graph model may include one or more machine-learned functions (e.g., an activation layer, etc.). As yet another example, a node of the machine-learned graph model may be configured to perform one or more algorithms on received data (e.g., image preprocessing, etc.).

In some implementations, the subset of nodes of a portion of the machine-learned graph model may include nodes of differing minimum processing capabilities. As an example, a first node of the subset of nodes may have a minimum processing capability including a floating point precision of 32 bits. A second node of the subset of nodes may have a minimum processing capability including a floating point precision of 64 bits. In some implementations, the minimum processing capability of the portion itself can be that of the highest individual node of its subset of nodes. To follow the previous example, the portion including the first node and the second node could have a minimum processing capability including a floating point precision of 64 bits. Alternatively, in some implementations, each node of a subset of nodes of a portion of the machine-learned graph model can have identical minimum processing capabilities.

In some implementations, the segmentation of the model to form the portions (e.g., the subsets of nodes) can be based on the minimum accuracy and/or speed required to perform the operations of the subsets of nodes. As an example, if each of the operations in a subset of nodes requires a floating point precision of FP64, the subset of nodes can be “cut” to be included in a portion so that the entire subset of nodes can be processed by a computation resource capable of processing at a precision of FP64. As another example, a first subset of nodes can have an associated processing latency. A second subset of nodes can be “cut” to be included in a second portion such that a third portion of nodes can receive the outputs of the first and second portions at an optimal time. More particularly, the subset of nodes can be included in the portion to minimize and/or optimize the latency of the distributed processing of the machine-learned graph model.

At (608), the method600A can include assigning the plurality of portions to the plurality of computation resources. For instance, a computing system (e.g., operations computing system190A) can assign, based on the minimum processing capabilities of the portions and the processing capabilities of the computation resources, each of the plurality of portions to a respective computation resource of the plurality of computation resources. As an example, a first portion can have a minimum processing capacity that includes a minimum floating point precision of 32 bits, while a second portion can have a minimum floating point precision of 16 bits. A first computation resource can have a processing capability that includes a maximum floating point precision of 16 bits. A second computation resource can have a processing capability that includes a maximum floating point precision of 32 bits. The first portion can be assigned to the second computation resource and the second portion can be assigned to the first computation resource. In such fashion, each of the computation resources can be utilized to an optimal degree to process portions of the machine-learned graph model.

FIG. 6Bdepicts a flowchart of a method600B for optimizing one or more portions of a segregated model to obtain an optimized machine-learned model according to example embodiments of the present disclosure. It should be noted that one or more portion(s) of the method600B can be performed subsequently to and/or concurrently with one or more portions of the method600A ofFIG. 6A. As an example, portion610of the method600B can directly follow the operations of portion608of the method600A ofFIG. 6A. Additionally, one or more portion(s) of the method600B can be implemented by one or more computing devices such as, for example, the computing devices described inFIGS. 1, 2, 4, and 5. Moreover, one or more portion(s) of the method600B can be implemented as an algorithm on the hardware components of the device(s) described herein (e.g., as inFIGS. 1, 2, 4, 7, and 8) to, for example, segregate and assign portions of a machine-learned model.FIG. 6Bdepicts elements performed in a particular order for purposes of illustration and discussion. Those of ordinary skill in the art, using the disclosures provided herein, will understand that the elements of any of the methods discussed herein can be adapted, rearranged, expanded, omitted, combined, and/or modified in various ways without deviating from the scope of the present disclosure.

At (610), the method600can include providing each of the portions to an assigned computation resource. For instance, a computing system (e.g., operations computing system190A) can provide each of the plurality of machine-learned portions to a respective computation resource to which the machine-learned portion is assigned. Each of the computation resources can be configured to process an assigned portion of the machine-learned graph model. In such fashion, the segregated machine-learned graph model (e.g., the plurality of portions) can be provided to the plurality of hardware resources for optimal distributed processing.

In some implementations, one or more connections can be first be generated between each of the portions to obtain a reconstructed machine-learned graph model. More particularly, the connections between nodes of the machine-learned graph model (e.g., the connections between the subsets of the nodes) can be restored to reconstruct the machine-learned graph model for processing. After obtaining the reconstructed machine-learned graph model, the model can be provided to the plurality of computation resources. The plurality of computation resources can be configured to process the reconstructed machine-learned graph model (e.g., according to the assignment of the portions to the each of the computation resources, etc.).

At (612), the method600can include obtaining data descriptive of a processing performance of each of the computation resources. For instance, a computing system (e.g., operations computing system190A) can obtain data descriptive of the processing performance of each of the computation resources of a vehicle computing system (e.g., vehicle computing system110) that processed the provided portions of the graph model. The processing performance can describe one or more processing metrics (e.g., a processing latency, a processing bandwidth, an accuracy of output data associated with the processing, etc.). As an example, the data descriptive of the processing performance may indicate that the output data from processing a portion by a computation resource was not accurate enough to facilitate proper operations (e.g., of an autonomous vehicle task, etc.). As another example, the data descriptive of the processing performance may indicate that the output data from processing of a portion by a computation resource can be less accurate and still facilitate proper operations. As such, the data descriptive of the processing performances can be utilized to further optimize the structure of the portions and/or the assignment of portions to computation resources.

At (614), the method600can include applying, based on the processing performances, optimization(s) to obtain an optimized machine-learned graph model. For instance, a computing system (e.g., operations computing system190A) can apply one or more optimizations to one or more respective portions of the graph model to obtain an optimized machine-learned graph model. The one or more optimizations can be based at least in part on the processing performance of the one or more computation resources assigned to process the one or more portions of the machine-learned graph model. The optimization(s) can include adjusting the minimum processing capability of the portion(s) and/or compressing a subset of nodes of the portion(s). As an example, a processing performance of a first computation resource assigned to a first portion may indicate that an output of the computation resource can be less accurate and can still facilitate proper operations. In response, the first portion can be optimized. For example, the subset of nodes of the first portion can be compressed (e.g., compressing layer(s) of a neural network, combining the operations of two nodes into one node, etc.). For another example, the minimum processing capability of the first portion can be reduced (e.g., reduced from a 64-bit floating point minimum precision to a 32-bit floating point minimum precision, etc.).

In some implementations, the one or more optimizations can be further based at least in part on an autonomous vehicle processing task respectively associated with the machine-learned portion. More particularly, the optimizations can be based on the proposed autonomous vehicle task output assigned to the portion of the machine-learned graph model. As an example, a portion may be associated with a data processing task and/or data preprocessing task (e.g., LIDAR data preprocessing and/or processing, image data preprocessing and/or processing, etc.). As another example, a portion may be associated with a certain ask (e.g., image segmentation, LIDAR segmentation, fused segmentation, detection, estimation, etc.). As yet another example, a portion may be associated with one or more mathematical operations. It should be noted that, in various implementations, the optimization(s) can occur either before or after the graph model is reconnected.

At (616), the method600can include providing the optimized machine-learned graph model to the respective plurality of computation resources for processing. For instance, a computing system (e.g., operations computing system190A) can provide the optimized machine-learned graph model to a computing resources of a vehicle computing system (e.g., system110) for processing. The computing resources of the vehicle computing system can process the plurality of portions to obtain an output different than the previous output generated before application of optimizations to the graph model.

At (618), the method600can include obtaining data indicative of an updated processing performance of at least one of the plurality of computation resources. The updated processing performance of the at least one computation resource can be different than the processing performance of the at least one computation resource. For instance, a computing system (e.g., operations computing system190A) can obtain data indicative of an updated processing performance for a computation resource. As an example, a first computation resource can process a first portion of the machine-learned graph model. Processing performance data can be obtained that describes the relative performance of the first computation resource. Optimization(s) can be applied to the first portion (e.g., compression of nodes included in the first portion, etc.) to generate an optimized first portion of an overall optimized machine-learned graph model. The first computation resource can obtain the optimized first portion and process the optimized first portion. Data associated with the processing of the optimized first portion can be obtained by the computing system that indicates that the processing performance of the first portion was greater after adjustments were applied to the first portion, therefore indicating that the machine-learned graph model has been optimized by the applied adjustments. In such fashion, the operations computing system (e.g., system190A) can iteratively apply adjustments to portions of the machine-learned graph model to fully optimized distributed processing of the segregated graph model.

FIG. 7depicts a flowchart of a method700for distributed processing of a segregated and optimized machine-learned graph model according to example embodiments of the present disclosure. One or more portion(s) of the method700can be implemented by one or more computing devices such as, for example, the computing devices described inFIGS. 1, 2, 4, and 5. Moreover, one or more portion(s) of the method700can be implemented as an algorithm on the hardware components of the device(s) described herein (e.g., as inFIGS. 1, 2, 4, 7, and 8) to, for example, perform distributed processing of an optimized segregated graph model.FIG. 7depicts elements performed in a particular order for purposes of illustration and discussion. Those of ordinary skill in the art, using the disclosures provided herein, will understand that the elements of any of the methods discussed herein can be adapted, rearranged, expanded, omitted, combined, and/or modified in various ways without deviating from the scope of the present disclosure.

At (702), the method700can include obtaining an optimized machine-learned graph model and portion assignment data. For instance, a computing system (e.g., vehicle computing system110) can obtain and implement the optimized machine-learned graph model onboard an autonomous vehicle according to portion assignment data. For example, the vehicle computing system (e.g., an autonomous vehicle computing system, etc.) can include a plurality of computation resources that include a respective plurality of processing capabilities. The computation resources and associated processing capabilities can be the same or substantially similar to those described previously. The computing system can obtain an optimized machine-learned graph model (e.g., the optimized graph model of portion614of the method600B, etc.). For example, the graph model may be accessed, received, retrieved, downloaded, stored, etc. onto the autonomous vehicle computing system (e.g., from an offboard repository, etc.). The graph model can include the plurality of portions, and can further include and/or be obtained in association with portion assignment data. The portion assignment data can describe an assignment of each of the portions to a respective computation resource.

The portion assignment data can be based at least in part on the plurality of processing capabilities of the computation resources and a minimum processing capability for each of the portions of the model. More particularly, the assignment data can include assignments of the portions to various computation resources such that the portions are assigned to computation resources that are sufficiently capable of processing the assigned portions. As an example, the portion assignment data can describe an assignment of a first portion with a minimum processing capability including a floating point precision of 64 bits to a computation resource with a processing capability including a floating point precision of 64 bits.

At (704), the method700can include obtaining input data from systems of an autonomous vehicle associated with a vehicle processing task. For instance, a computing system (e.g., vehicle computing system110) can obtain input data from one or more systems of the vehicle105(e.g., perception system170A, prediction system170B, motion planning system170C, remote computing system190B, operations computing system190A, sensor system135, secondary vehicle computing system, autonomy computing system140, etc.). The input data can be associated with an autonomous vehicle task. As an example, the input data can be associated with a perception task (e.g., a task to perceive an environment about the autonomous vehicle, etc.). As another example, the input data can be associated with a prediction task (e.g., a task to predict future event(s) regarding the environment or one or more objects about the autonomous vehicle, etc.). As yet another example, the input data can be associated with a motion planning task (e.g., a task to plan the motion of the vehicle in response to perceived and/or predicted event(s) regarding the environment or one or more objects about the autonomous vehicle, etc.).

At (706), the method700can include processing the input data with the graph model based on the portion assignment data to obtain output data. For instance, a computing system (e.g., vehicle computing system110) can process the input data with the optimized machine-learned graph model based at least in part on the portion assignment data to obtain output data. The output data can be associated with the autonomous vehicle processing task. As an example, the autonomous vehicle processing task can be a perception task and the output data can be perception data (e.g., identification of object(s) in an environment about the autonomous vehicle, etc.). As another example, the autonomous vehicle processing task can be a prediction task and the output data can be prediction data (e.g., predicted movement of the one or more objects perceived in the environment about the autonomous vehicle, etc.). As yet another example, the autonomous vehicle processing task can be a motion planning task and the output data can be motion planning data (e.g., data to control motion of the autonomous vehicle in response to the predicted movement of object(s) perceived to be in the environment about the autonomous vehicle, etc.).

In some implementations, the computing system can process the input data with the optimized machine-learned graph model by processing each portion of the model with the computation resource that is assigned to each portion by the portion assignment data. As an example, a first portion can be assigned to a first computation resource and a second portion can be assigned to a second computation resource. The first portion can be processed with the first computation resource and the second portion can be processed by the second computation resource.

At (708A), the method700can include providing the output data to one or more additional systems of the autonomous vehicle. For instance, a computing system (e.g., vehicle computing system110) can provide the output data to one or more additional systems of the autonomous vehicle. As an example, the motion planning data can be provided to a vehicle control system (e.g., and/or an associated intermediate interface, etc.) of the autonomous vehicle to control the autonomous vehicle in accordance with the motion planning data.

At (708B), the method700can include providing data descriptive of a processing performance of the computation resources to a computing system associated with a service entity. It should be noted that the operations associated with portion708B can occur concurrently or subsequently to the operations associated with portion708A of method700. For instance, a computing system (e.g., vehicle computing system110) can provide data descriptive of the processing performance of the computation resources to a computing system associated with a service entity (e.g., operations computing system190A of service entity185). Further, the autonomous vehicle processing task can be associated with a service provided and/or facilitated by the service entity. As an example, the service entity can facilitate an autonomous vehicle service (e.g., a transportation service, delivery service, rideshare service, etc.). The autonomous vehicle task can be associated with the autonomous vehicle service (e.g., a motion planning task for operating the autonomous vehicle to fulfill the autonomous vehicle service). The computing system to which the data is provided can be associated with the service entity. For example, the computing system may be a computing system utilized by the service entity to optimize the optimized machine-learned graph model and to generate the portion assignment data (e.g., according to the methods of the present disclosure, etc.).

At (710B), the method700can include obtaining an updated machine-learned graph model and/or updated portion assignment data. For instance, a computing system (e.g., vehicle computing system110) can, in response to providing the data descriptive of the processing performance, obtain an updated machine-learned graph model different from the first optimized model and/or updated portion assignment data different than the portion assignment data. As an example, the provided data may indicate a poor processing performance associated with the processing of a first portion by a first computation resource. In response, the updated assignment data can reassign the first portion to a different computation resource. Alternatively, or additionally, the updated machine-learned graph model may include further optimizations to the optimized model. To follow the previous example, the updated machine-learned graph model may include an increased minimum processing capability for the first portion and/or may have compressed the first portion (e.g., consolidated one or more neural network layers, reassigned one or more neural units from the first portion to a different portion, etc.).

Various means can be configured to perform the methods and processes described herein.FIG. 8depicts example units associated with a computing system for performing operations and functions according to example embodiments of the present disclosure. As depicted,FIG. 8depicts a computing system800that can include, but is not limited to, graph model obtaining unit(s)805; processing capability determination unit(s)810; portion determination unit(s)815; and portion assigning unit(s)820.

In some implementations, one or more of the units may be implemented separately. In some implementations, one or more units may be a part of or included in one or more other units. These means can include processor(s), microprocessor(s), graphics processing unit(s), logic circuit(s), dedicated circuit(s), application-specific integrated circuit(s), programmable array logic, field-programmable gate array(s), controller(s), microcontroller(s), and/or other suitable hardware. The means can also, or alternately, include software control means implemented with a processor or logic circuitry, for example. The means can include or otherwise be able to access memory such as, for example, one or more non-transitory computer-readable storage media, such as random-access memory, read-only memory, electrically erasable programmable read-only memory, erasable programmable read-only memory, flash/other memory device(s), data registrar(s), database(s), and/or other suitable hardware.

The means can be programmed to perform one or more algorithm(s) for carrying out the operations and functions described herein (including the claims). For instance, the means can be configured to obtain a graph model (e.g., a machine-learned graph neural network, etc.). A graph model obtaining unit805is an example of means for obtaining a machine-learned graph model as described herein.

The means can be configured to determine a processing capability for a plurality of a respective plurality of computation resources. For example, the means can be configured to determine that each of a plurality of computation resources has a certain processing capability. A processing capability determination unit810is one example of a means for determining a processing capability for a plurality of a respective plurality of computation resources as described herein.

The means can be configured to determine a plurality of portions from a machine-learned graph model. For example, the means can be configured to segregate a machine-learned graph neural network into subsets of network nodes. A portion determination unit815is one example of a means for determining a plurality of portions from a machine-learned graph model as described herein.

The means can be configured to assign a plurality of portions of a machine-learned graph model to a plurality of computation resources. For example, the means can be configured to assign each of the plurality of machine-learned portions to a respective computation resource of the plurality of computation resources. A portion assigning unit820is one example of a means for assigning a plurality of portions of a machine-learned graph model to a plurality of computation resources as described herein.

FIG. 9depicts a block diagram of an example computing system1000according to example embodiments of the present disclosure. The example system1000includes a computing system1100and a machine learning computing system1200that are communicatively coupled over one or more networks1300.

In some implementations, the computing system1100can perform distributed processing of a segregated machine-learned graph model. In some implementations, the computing system1100can be included in an autonomous vehicle. For example, the computing system1100can be on-board the autonomous vehicle. In other implementations, the computing system1100is not located on-board the autonomous vehicle. For example, the computing system1100can be or otherwise include one or more remote backend services that can perform distributed processing of a segregated graph model to obtain an output associated with provision of the remote backend service(s). The computing system1100can include one or more distinct physical computing devices.

The computing system1100can include computing device(s)1105, which can include one or more processors1110and a memory1115. The one or more processors1110can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, a FPGA, a controller, a microcontroller, a graphics processing unit, an accelerator, a tensor processing unit, etc.) and can be one processor or a plurality of processors that are operatively connected. The memory1115can include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, one or more memory devices, flash memory devices, etc., and combinations thereof.

The processor(s) and/or memory1115can be included in a plurality of computation resources of the computing system1100. More particularly, computing devices (e.g., FPGAs, CPUs, CPU cores, GPUs, etc.) and/or processors of the computing system1000can be dynamically partitioned as computation resource(s). As an example, the computation resource(s)402could be or otherwise include one or more of the processor(s)1110(e.g., an FPGA, a GPU and a CPU, etc.). The computation resources could then be dynamically partitioned to further include one or more cores of a central processing unit. Other resources (e.g., FPGAs, memory1115, etc.) could be included in a different computation resource. Dynamic partitioning of various computing devices or portions of computing devices into a computation resource can be based on a variety of factors, including but not limited to available hardware resources, hardware failure(s), anticipated (e.g., predicted) processing operation(s), received data types and/or quantities, etc. of the computing system1100.

The memory1115can store information that can be accessed by the one or more processors1110. For instance, the memory1115(e.g., one or more non-transitory computer-readable storage mediums, memory devices) can store data1120that can be obtained, received, accessed, written, manipulated, created, and/or stored. The data1120can include, for instance, a segregated machine-learned model (e.g., an optimized machine-learned graph model, etc.) that includes a plurality of portions (e.g., optimized portions of a graph model, etc.), alongside portion assignment data that describes assignments of the portions to respective computation resources (e.g., groupings of processors1110) of the computing system1100as described herein. In some implementations, the computing system1100can obtain data from one or more memory device(s) that are remote from the computing system1100. As an example, the computing system1100can receive an optimized machine-learned graph model and portion assignment data from the machine-learning computing system1200via network(s)1300(e.g., utilizing communication interface1130, etc.).

The memory1115can also store computer-readable instructions1125that can be executed by the one or more processors1120. The instructions1125can be software written in any suitable programming language or can be implemented in hardware. Additionally, or alternatively, the instructions1125can be executed in logically and/or virtually separate threads on processor(s)1110.

For example, the memory1115can store instructions1125that when executed by the one or more processors1110cause the one or more processors1110(the computing system) to perform any of the operations and/or functions described herein, including, for example, processing the obtained plurality of portions of the graph model with the computation resources of the computing system1100according to the obtained portion assignment data (e.g., processing a first portion with a first computation resource based on a described assignment of the portion assignment data, etc.).

According to an aspect of the present disclosure, the computing system1100can store or include one or more machine-learned models1135. As examples, the machine-learned models1135can be or can otherwise include various machine-learned models such as, for example, neural networks (e.g., deep neural networks), support vector machines, decision trees, ensemble models, k-nearest neighbors models, Bayesian networks, or other types of models including linear models and/or non-linear models. Example neural networks include feed-forward neural networks, recurrent neural networks (e.g., long short-term memory recurrent neural networks), convolutional neural networks, or other forms of neural networks.

More particularly, the model(s)1135can include one or more graph models. Graph model(s) (e.g., graph neural networks, etc.), can include deep learning graph neural networks. Additionally, or alternatively, a graph model can be a model utilizing a model architecture that includes a plurality of machine-learned models organized as a directed graph. As an example, the machine-learned graph model can be associated with an autonomous vehicle processing task such as perception, and the machine-learned graph model can include a plurality of trained models (e.g., convolutional neural network(s), recurrent neural network(s), SVMs, etc.) that are configured to generate outputs associated with a plurality of perception subtasks. The graph model can organize communication between the trained models so that outputs from one model can be obtained and processed by a next node of the graph (e.g., a model) according to the edges connecting the nodes. In such fashion, the graph model can, in some implementations, include a plurality of trained models, and can be distributed across a plurality of computation resources such that each resource processes a trained model. Additionally, or alternatively, in some implementations, each of the plurality of trained models organized in the graph model architecture can be further segregated into portions, such that each model includes a plurality of portions that can be processed by a respective computation resource of the computing system1100.

In some implementations, the computing system1100can receive the one or more machine-learned models1135from the machine learning computing system1200over network(s)1300and can store the one or more machine-learned models1135in the memory1115. The computing system1100can then use or otherwise implement the one or more machine-learned models1135(e.g., by processor(s)1110). In particular, the computing system1100can implement the machine learned model(s)1135to perform distributed processing of the graph model to obtain an output (e.g., an output associated with an autonomous vehicle processing task, etc.).

The machine learning computing system1200can include one or more computing devices1205. The machine learning computing system1200can include one or more processors1210and a memory1215. The one or more processors1210can be any suitable processing device (e.g., a processor core, a microprocessor, an ASIC, a FPGA, a controller, a microcontroller, etc.) and can be one processor or a plurality of processors that are operatively connected. The memory1215can include one or more non-transitory computer-readable storage media, such as RAM, ROM, EEPROM, EPROM, one or more memory devices, flash memory devices, etc., and combinations thereof.

The memory1215can store information that can be accessed by the one or more processors1210. For instance, the memory1215(e.g., one or more non-transitory computer-readable storage mediums, memory devices) can store data1220that can be obtained, received, accessed, written, manipulated, created, and/or stored. The data1220can include, for instance, the graph model and/or portion assignment data as described herein. In some implementations, the machine learning computing system1200can obtain data from one or more memory device(s) that are remote from the machine learning computing system1200.

The memory1215can also store computer-readable instructions1225that can be executed by the one or more processors1210. The instructions1225can be software written in any suitable programming language or can be implemented in hardware. Additionally, or alternatively, the instructions1225can be executed in logically and/or virtually separate threads on processor(s)1210.

For example, the memory1215can store instructions1225that when executed by the one or more processors1210cause the one or more processors1210(the computing system) to perform any of the operations and/or functions described herein, including, for example, segregation of a graph model, generation of portion assignment data, etc.

In some implementations, the machine learning computing system1200includes one or more server computing devices. If the machine learning computing system1200includes multiple server computing devices, such server computing devices can operate according to various computing architectures, including, for example, sequential computing architectures, parallel computing architectures, or some combination thereof.

In addition to, or alternatively to the model(s)1235at the computing system1100, the machine learning computing system1200can include one or more machine-learned models1235. As examples, the machine-learned models1235can be or can otherwise include various machine-learned models such as, for example, the model(s)1135obtained by the computing system110from the machine-learning computing system1200.

As an example, the machine learning computing system1200can communicate with the computing system1100according to a client-server relationship. For example, the machine learning computing system1200can implement the machine-learned models1235to provide a web service to the computing system1100. For example, the web service can provide a method for segregation and optimization of machine-learned models. As an example, the computing system1100can provide a graph model (e.g., model(s)1135) to the machine-learning computing system1200. The machine-learning computing system1200can obtain the graph model and segregate the graph model into a plurality of portions (e.g., utilizing model trainer1240, etc.). The machine-learning computing system1200can additionally assign each of the portions to a computation resource of the computing system (e.g., computing device(s)1105, etc.), and the assignments can be described by portion assignment data (e.g., stored in memory1215). The portions of the graph model and the portion assignment data can be provided to the computing system1100. Additionally, the machine-learned computing system1200can iteratively communicate with the computing system1100to optimize the distribution of the graph model based on processing performance of the computation resources (e.g., computing device(s)1105) of the computing system1100(e.g., as described with reference to the method700ofFIG. 7).

Thus, machine-learned models1135can be located and used at the computing system1100and/or machine-learned models1235can be located and used at the machine learning computing system1200.

In some implementations, the machine learning computing system1200and/or the computing system1100can train the machine-learned models1135and/or1235through use of a model trainer1240. The model trainer1240can train the machine-learned models1135and/or1240using one or more training or learning algorithms. One example training technique is backwards propagation of errors. In some implementations, the model trainer1240can perform supervised training techniques using a set of labeled training data. In other implementations, the model trainer1240can perform unsupervised training techniques using a set of unlabeled training data. The model trainer1240can perform a number of generalization techniques to improve the generalization capability of the models being trained. Generalization techniques include weight decays, dropouts, or other techniques.

It should be noted that the graph model of the present embodiments can be trained at the machine-learning computing system1200prior to inference-phase utilization of the model and/or segregation of the model. As such, segregation of model(s) by the computing system1200will generally occur subsequently to training and optimization of the unsegregated models using the model trainer1240and the training data1245.

In particular, the model trainer1240can train a machine-learned model1135and/or1235based on a set of training data1245. The model trainer1240can be implemented in hardware, firmware, and/or software controlling one or more processors.

The computing system1100and the machine learning computing system1200can each include a communication interface1130and1250, respectively. The communication interfaces1130/1250can used to communicate with one or more systems or devices, including systems or devices that are remotely located from the computing system1100and the machine learning computing system1200. A communication interface1130/1250can include any circuits, components, software, etc. for communicating with one or more networks (e.g.,1300). In some implementations, a communication interface1130/1250can include, for example, one or more of a communications controller, receiver, transceiver, transmitter, port, conductors, software and/or hardware for communicating data.

FIG. 9illustrates one example computing system1000that can be used to implement the present disclosure. Other computing systems can be used as well. For example, in some implementations, the computing system1100can include the model trainer1240and the training dataset1245. In such implementations, the machine-learned models1240can be both trained and used locally at the computing system1100. As another example, in some implementations, the computing system1100is not connected to other computing systems.

In addition, components illustrated and/or discussed as being included in one of the computing systems1100or1200can instead be included in another of the computing systems1100or1200. Such configurations can be implemented without deviating from the scope of the present disclosure. The use of computer-based systems allows for a great variety of possible configurations, combinations, and divisions of tasks and functionality between and among components. Computer-implemented operations can be performed on a single component or across multiple components. Computer-implemented tasks and/or operations can be performed sequentially or in parallel. Data and instructions can be stored in a single memory device or across multiple memory devices.

Computing tasks discussed herein as being performed at computing device(s) remote from the vehicle can instead be performed at the vehicle (e.g., via the vehicle computing system), or vice versa. Such configurations can be implemented without deviating from the scope of the present disclosure. The use of computer-based systems allows for a great variety of possible configurations, combinations, and divisions of tasks and functionality between and among components. Computer-implemented operations can be performed on a single component or across multiple components. Computer-implemented tasks and/or operations can be performed sequentially or in parallel. Data and instructions can be stored in a single memory device or across multiple memory devices.