RNN Runtime Configuration Automation for Time-Memory Tradeoffs
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Solution Overview
Problem
The efficient execution of recurrent neural networks (RNNs) is hindered by the challenging and error-prone process of selecting optimal runtime configurations, particularly in dynamic computing environments, where choices between looped and unlooped execution patterns impact performance due to tradeoffs between memory usage and execution time.
Innovation Solution
A system automatically determines runtime configurations for RNNs by analyzing factors such as the type of RNN, network structure, input data characteristics, and available computing resources, allowing for dynamic selection between rolled and unrolled execution patterns to optimize performance.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Speed
If RNN is executed using an unrolled execution pattern, then execution speed is improved, but memory resource consumption increases
Solution Approach 1:
The patent implements dynamic selection between rolled and unrolled execution patterns based on real-time analysis of computing resources and data characteristics. The system adjusts the execution pattern dynamically rather than using a fixed approach, allowing optimization of both speed and memory usage according to current operational conditions.
Solution Approach 2:
The system changes execution parameters (rolled vs. unrolled pattern) based on analyzed factors including RNN type, network structure, input data characteristics, and available computing resources. This parameter adjustment resolves the contradiction by selecting the appropriate execution pattern for specific operational contexts.
2Productivity
If manual configuration selection is performed for RNN runtime settings, then execution performance can be optimized, but the process becomes cumbersome and error-prone
Solution Approach 1:
The patent implements an automated configuration system that performs self-analysis of RNN characteristics and computing resources to determine optimal runtime settings. This eliminates the need for manual configuration by data scientists, reducing errors and operational complexity while maintaining performance optimization.
Solution Approach 2:
The system incorporates feedback mechanisms that analyze various factors (RNN type, network structure, input characteristics, resource availability) to automatically adjust configuration settings. This closed-loop approach replaces manual tuning with automated decision-making based on real-time system state.
3Adaptability or versatility
If a fixed execution pattern is used for all RNNs, then configuration simplicity is maintained, but performance optimization in dynamic environments is lost
Solution Approach 1:
The patent transforms the static configuration approach into a dynamic one by continuously analyzing operational conditions and adjusting execution patterns accordingly. This enables the system to adapt to changing environments while maintaining manageable complexity through automated decision-making.
Data Source
AI summary
Techniques are described automatically determining runtime configurations used to execute recurrent neural networks (RNNs) for training or inference. One such configuration involves determining whether to execute an RNN in a looped, or “rolled,” execution pattern or in a non-looped, or “unrolled,” execution pattern. Execution of an RNN using a rolled execution pattern generally consumes less memory resources than execution using an unrolled execution pattern, whereas execution of an RNN using an unrolled execution pattern typically executes faster. The configuration choice thus involves a time-memory tradeoff that can significantly affect the performance of the RNN execution. This determination is made automatically by a machine learning (ML) runtime by analyzing various factors such as, for example, a type of RNN being executed, the network structure of the RNN, characteristics of the input data to the RNN, an amount of computing resources available, and so forth.