Automated RNN Architecture Generation via Domain Specific Language
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Solution Overview
Problem
The design of recurrent neural network (RNN) architectures is slow, costly, and laborious due to the extensive exploration of potential architectures by human experts, often resulting in sub-optimal designs.
Innovation Solution
An automated system using a domain-specific language (DSL) to generate and evaluate RNN architectures, comprising a candidate architecture generator, ranking module, evaluation module, and DSL compiler, which optimizes and selects architectures based on performance, leveraging machine-readable syntax and operators like GRU, LSTM, and QRNN.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If human experts manually design RNN architectures through extensive exploration, then the design process can leverage human intuition and expertise, but the process becomes slow, costly, and laborious
Solution Approach 1:
The system enables self-service by allowing the automated architecture generator to independently explore and evaluate RNN architectures without continuous human intervention. The generator uses machine-readable syntax and automatic evaluation metrics to autonomously identify optimal architectures, eliminating the need for slow manual expert exploration while maintaining high design quality.
Solution Approach 2:
The patent replaces the mechanical process of manual human design with an automated computational system. The architecture generator uses algorithmic approaches and automated evaluation to substitute human expert manual work, significantly reducing design time while maintaining or improving architecture quality through systematic exploration of the design space.
2Adaptability or versatility
If human experts explore extensive architecture spaces with minor modifications, then optimal architectures can be discovered, but the process becomes costly and laborious
Solution Approach 1:
The system introduces dynamics by enabling flexible, programmatic modification of architecture parameters through machine-readable syntax. The architecture generator can dynamically adjust hyperparameters, layer configurations, and connectivity patterns, allowing extensive exploration of architecture spaces with minor modifications while automating the effort through computational processes rather than manual labor.
Solution Approach 2:
The patent leverages parameter changes by systematically varying architecture hyperparameters, layer sizes, activation functions, and connectivity patterns through automated generation. The machine-readable syntax enables efficient parameter exploration and modification, allowing the system to evaluate numerous architecture variations with minimal manual effort while maintaining adaptability to different tasks and datasets.
3Productivity
If conventional techniques are used for designing neural network architectures, then the process can be performed with existing tools, but the results are often sub-optimal
Solution Approach 1:
The system implements feedback through automatic evaluation of generated architectures using performance metrics on validation datasets. The architecture generator receives feedback from evaluation results and uses this information to guide further generation and refinement, iteratively improving architecture performance while maintaining high productivity through automated evaluation loops rather than manual assessment.
Data Source
AI summary
A system automatically generates recurrent neural network (RNN) architectures for performing specific tasks, for example, machine translation. The system represents RNN architectures using a domain specific language (DSL). The system generates candidate RNN architectures. The system predicts performances of the generated candidate RNN architectures, for example, using a neural network. The system filters the candidate RNN architectures based on their predicted performance. The system generates code for selected a candidate architectures. The generated code represents an RNN that is configured to perform the specific task. The system executes the generated code, for example, to evaluate an RNN or to use the RNN in an application.