Machine Learning Resource Allocation Through Deterministic Compilation
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Solution Overview
Problem
The unpredictable resource allocation and performance variation across devices executing machine learning tasks make it difficult to determine the exact resources required by machine learning models, leading to inefficiencies in scheduling and pricing.
Innovation Solution
A system that determines resource requirements of machine learning models at compile-time using a deterministic instruction set architecture, allowing for efficient scheduling and allocation on special purpose machine learning processors.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Adaptability or versatility
If machine learning models are executed on general-purpose processing devices with optimization, then device adaptability is improved, but resource usage becomes unpredictable
Solution Approach 1:
The patent segments the machine learning processing into two distinct phases: a compile-time phase that determines resource requirements through deterministic analysis, and a runtime phase that executes the model with guaranteed performance. This segmentation allows the model to be optimized for specific devices while maintaining predictable resource usage by pre-calculating requirements during compilation.
Solution Approach 2:
The patent performs resource requirement determination as a preliminary action during the compile-time phase, before the model is executed. By analyzing the model's computational graph and determining operations, memory, and I/O requirements in advance, the system establishes predictable resource bounds that guide efficient scheduling and allocation on target devices.
2Productivity
If resource allocation is optimized for each device, then processing efficiency is improved, but scheduling reliability deteriorates
Solution Approach 1:
The system performs preliminary resource determination during compilation, calculating the exact operations, memory, and I/O requirements for each machine learning model before execution. This advance knowledge enables reliable scheduling decisions while maintaining processing efficiency, as the scheduler can confidently allocate resources based on predetermined requirements rather than estimates.
Solution Approach 2:
The patent establishes a feedback mechanism where resource determination results from compile-time analysis are fed into the scheduling system. This feedback loop allows the scheduler to make informed decisions about model placement and resource allocation, ensuring both efficient utilization of processing devices and reliable meeting of performance targets.
3Measurement precision
If deterministic resource determination is implemented, then scheduling accuracy is improved, but system complexity increases
Solution Approach 1:
The patent divides the system into distinct functional components: a compile-time resource determination module that analyzes computational graphs and calculates requirements, and a runtime execution module that follows predetermined allocation instructions. This segmentation manages complexity by confining complex analysis to compilation time, allowing simple, reliable execution at runtime.
Solution Approach 2:
The patent introduces an intermediary compilation phase that acts as a mediator between model development and execution. During this intermediate stage, the system performs deterministic resource analysis and generates execution instructions, translating complex model specifications into simple, actionable resource allocation plans that guide efficient and accurate scheduling.
Data Source
AI summary
A method for allocating resources for a machine learning model is disclosed. A machine learning model to be executed on a special purpose machine learning model processor is received. A computational data graph is generated from the machine learning model. The computational dataflow graph represents the machine learning model which includes nodes, connector directed edges, and parameter directed edges. The operations of the computational dataflow graph is scheduled and then compiled using a deterministic instruction set architecture that specifies functionality of a special purpose machine learning model processor. An amount of resources required to execute the computational dataflow graph is determined. Resources are allocated based on the determined amounts of resources required to execute the machine learning model represented by the computational dataflow graph.