Autonomous Vehicle Compute Offloading Over Wireless Vehicle Networks
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Solution Overview
Problem
Current data processing systems, particularly in graphics and machine learning operations, face inefficiencies due to limitations in parallel processing capabilities and interconnect technologies, which hinder the effective acceleration of graphics and machine learning workloads.
Innovation Solution
A computing system architecture that integrates a graphics processing unit (GPU) communicatively coupled to host processor cores via high-speed interconnects like PCIe or NVLink, allowing for efficient allocation and processing of workloads through dedicated circuitry, and utilizing a unified memory architecture for simplified programmability and reduced latency.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If traditional fixed function computational units are used in graphics processors, then processing of specific graphics operations is reliable, but processing versatility and adaptability to different workloads are limited
Solution Approach 1:
The patent implements a unified processor architecture that can dynamically execute different types of computational workloads including graphics processing, machine learning inference, and general-purpose computing tasks. The processor uses a single instruction set and unified memory architecture to handle multiple workload types without requiring separate specialized hardware units for each function type.
Solution Approach 2:
The processor architecture employs dynamic workload classification and routing mechanisms that adaptively direct different types of computational tasks to appropriate execution pipelines. The system can dynamically switch between different processing modes and allocate resources based on the specific requirements of the current workload, rather than being statically configured for a single function.
2Speed
If separate memory architectures are used for graphics processor and host processor, then memory access is simple, but data transfer latency and communication overhead increase
Solution Approach 1:
The patent implements a unified memory architecture where the graphics processor and host processor share a common memory space through high-speed interconnects such as PCIe or NVLink. This allows both processors to access the same memory resources simultaneously, eliminating the need for frequent data copies between separate memory spaces and reducing communication overhead.
Solution Approach 2:
The system introduces a unified memory interface and address translation mechanism that mediates between the graphics processor and host processor memory access requests. This intermediary layer manages memory allocation, handles address mapping, and coordinates access between the two processors to prevent conflicts while maintaining high transfer speeds.
3Productivity
If parallel processing capabilities are increased, then workload acceleration is improved, but coordination overhead and processing synchronization complexity increase
Solution Approach 1:
The patent divides parallel processing tasks into independent thread blocks that can execute asynchronously on multiple processing cores. Each thread block is self-contained with its own data and computation requirements, allowing the system to maximize parallel execution without requiring complex synchronization between threads. The segmentation of work into independent units reduces coordination overhead while maintaining high productivity.
Data Source
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AI summary
One embodiment provides for a computing device within an autonomous vehicle, the compute device comprising a wireless network device to enable a wireless data connection with an autonomous vehicle network, a set of multiple processors including a general-purpose processor and a general-purpose graphics processor, the set of multiple processors to execute a compute manager to manage execution of compute workloads associated with the autonomous vehicle, the compute workload associated with autonomous operations of the autonomous vehicle, and offload logic configured to execute on the set of multiple processors, the offload logic to determine to offload one or more of the compute workloads to one or more autonomous vehicles within range of the wireless network device.