Dynamic Rollout Control for Parallel Machine Learning Layer Execution
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Solution Overview
Problem
Current machine learning systems, particularly deep neuronal networks, operate sequentially, leading to slow calculations due to dependency on previous node outputs, resulting in slower response times and inefficient use of processing power.
Innovation Solution
Introducing a method to control the calculation of machine learning systems by assigning control variables to connections and layers, allowing for decoupled calculations and parallel processing, including the use of skip connections and recurrent connections to enable asynchronous and parallel computation of intermediate variables.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If sequential calculation of nodes is used, then the calculation order is simple and easy to control, but the response time is slow and processing efficiency is low
Solution Approach 1:
The patent introduces a dynamic rollout mechanism that transforms the static sequential execution into a dynamic parallel execution model. The rollout assigns different calculation orders to different nodes based on their dependencies, enabling simultaneous computation of independent nodes while maintaining controlled execution flow through the rollout sequence.
Solution Approach 2:
The patent segments the graph nodes into different calculation groups based on their dependency relationships. By dividing nodes into segments that can be calculated independently according to the rollout, the system enables parallel processing of multiple node segments simultaneously, reducing overall calculation time while maintaining simplicity in control.
2Ease of operation
If sequential calculation of nodes is used, then the calculation flow is easy to manage, but the processing power utilization is inefficient
Solution Approach 1:
The rollout mechanism dynamically determines the calculation order of nodes, transforming the static sequential flow into a dynamic parallel execution model. This allows multiple processing units to work simultaneously on different node segments, significantly improving processing power utilization while the rollout maintains manageable control over the calculation flow.
Solution Approach 2:
The patent introduces a new dimension of parallel execution by assigning rollout values to nodes, creating a multi-dimensional execution space where nodes with the same rollout value can be processed simultaneously. This dimensional transformation enables efficient utilization of multiple processing units while keeping the calculation flow manageable through the rollout hierarchy.
3Speed
If complete parallelization of layers is implemented, then the calculation speed increases significantly, but the system complexity increases
Solution Approach 1:
The rollout mechanism provides a dynamic control approach that enables parallelization without proportionally increasing system complexity. By using a simple rollout value assignment and comparison logic, the system achieves complex parallel execution patterns that would otherwise require sophisticated scheduling mechanisms, thus improving calculation speed while limiting complexity growth.
Solution Approach 2:
The system uses self-service mechanisms where each node independently determines its execution timing based on its rollout value and the availability of input data. This decentralized self-service approach enables automatic parallelization without requiring complex centralized scheduling, thereby increasing calculation speed while keeping system complexity manageable through simple local decision-making rules.
Data Source
AI summary
A device for operating a machine learning system. The machine learning system is assigned a predefinable rollout, which characterizes a sequence in which each of the layers ascertains an intermediate variable. When assigning the rollout, each connection or each layer is assigned a control variable, which characterizes whether the intermediate variable of each of the subsequent connected layers is ascertained according to the sequence or regardless of the sequence. A calculation of an output variable of the machine learning system as a function of an input variable of the machine learning system is controlled as a function of the predefinable rollout. Also described is a method for operating the machine learning system.