Distributed Code Execution for Real-Time Marine Edge Computing
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Solution Overview
Problem
Robots operating in remote locations face hardware and connectivity limitations, such as limited memory, power, and computing resources, making it difficult to process and transmit data effectively.
Innovation Solution
A method and system for generating and executing distributed code using a large language model (LLM) to conditionally assign code portions to multiple computing devices based on their capabilities, allowing parallel execution across a network, with an execution engine coordinating the process.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If data processing is performed locally on the robot, then processing speed and real-time response are improved, but hardware resource consumption (memory, power, computing) increases beyond available capacity
Solution Approach 1:
The patent segments data processing tasks into multiple independent code portions that can be distributed across different computing devices. The system divides the robot's data processing workload into separate functions that execute on edge devices, cloud servers, or local hardware, allowing real-time processing without concentrating all computational demands on the robot's limited onboard resources.
2Productivity
If sophisticated data processing algorithms are executed on the robot, then data analysis capability is improved, but device complexity and hardware requirements increase
Solution Approach 1:
The patent introduces an intermediary execution engine and code generation system that translates high-level data processing requirements into distributed code portions. This intermediary layer manages the complexity of sophisticated algorithms by automatically generating and coordinating execution across multiple devices, shielding the robot from direct hardware complexity while maintaining advanced data analysis capabilities.
3Productivity
If all code is executed on a single device, then system simplicity is maintained, but processing efficiency and scalability are limited
Solution Approach 1:
The patent creates a universal code execution framework where the same code generation and distribution system can adapt to various hardware configurations and network environments. The execution engine automatically determines the optimal distribution of code portions across different device types (edge devices, cloud servers, local processors), providing processing efficiency and scalability without requiring complex manual configuration for each scenario.
Data Source
AI summary
Systems and methods for executing code. The systems and methods include generating code by querying a model wherein one portion of the code is conditioned on an output on a second portion of the code, analyzing functions in the code with a second model to evaluate opportunities to implement tasks on devices, marking the code to designate portions of the code that can be performed on the devices, and assigning portions of the code to the devices based on capabilities of the devices. The systems and methods further include distributing the portions of the code to the devices, wherein the conditional portion of the code is distributed onto a first device and the second portion is distributed onto an edge device, executing the code across the devices, and transmitting data collected by the edge device to the first device upon meeting the condition in the code.