Deep Learning Network Construction via Intermediate Representation
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Solution Overview
Problem
Existing deep learning network construction methods require technical knowledge and coding skills, making it difficult for users with limited expertise to build accurate models, and are often platform-dependent, prone to errors due to misplaced layers.
Innovation Solution
A computer-implemented method that extracts user-provided multi-modal inputs to generate and design deep learning networks, creating an intermediate representation that can be automatically converted into source code, allowing for platform-agnostic model building and validation, even for users with limited coding expertise, using a drag-and-drop interface and cognitive systems for intelligent model design.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Ease of operation
If existing network construction approaches with libraries are used, then coding capability is leveraged, but technical knowledge requirements and platform dependency increase
Solution Approach 1:
The patent introduces an intermediate representation (IR) as a mediator between high-level user specifications and low-level platform-specific code. The IR serves as a platform-agnostic intermediate form that captures the essential structure and semantics of neural networks without being tied to any specific deep learning framework, thereby reducing technical knowledge requirements while maintaining ease of construction
Solution Approach 2:
The system segments the model construction process into distinct phases: user specification, intermediate representation generation, and code synthesis. This segmentation allows users to work at a high level without needing to understand underlying platform complexities, while the system handles the complex transformations automatically
2Reliability
If manual network construction is performed, then model accuracy can be optimized, but error probability increases due to misplaced layers
Solution Approach 1:
The system performs self-validation and automatic correction of construction errors through the intermediate representation. The IR enables automated checking of layer placement correctness and logical consistency, allowing the system to self-correct mistakes without requiring manual verification, thereby maintaining high accuracy while improving efficiency
Solution Approach 2:
The intermediate representation enables automated feedback mechanisms that validate layer placements and network architecture decisions. The system can automatically detect and correct misplaced layers by comparing the constructed model against the specified requirements in the IR, providing immediate feedback without manual intervention
3Adaptability or versatility
If platform-specific libraries are used, then coding flexibility is available, but portability across platforms decreases
Solution Approach 1:
The intermediate representation is designed as a universal, platform-agnostic format that can represent neural networks independent of any specific deep learning framework. This universal IR enables the same model specification to be translated to multiple different platforms (PyTorch, TensorFlow, Keras, etc.), providing broad platform compatibility while eliminating platform dependency in the construction phase
Solution Approach 2:
The IR acts as an intermediary layer that decouples the model specification from platform-specific implementations. By translating user requirements into a platform-independent IR first, then generating platform-specific code from the IR, the system achieves high portability without sacrificing the ability to leverage platform-specific features when needed
Data Source
AI summary
Methods, systems, and computer program products for multi-modal construction of deep learning networks are provided herein. A computer-implemented method includes extracting, from user-provided multi-modal inputs, one or more items related to generating a deep learning network; generating a deep learning network model, wherein the generating includes inferring multiple details attributed to the deep learning network model based on the one or more extracted items; creating an intermediate representation based on the deep learning network model, wherein the intermediate representation includes (i) one or more items of data pertaining to the deep learning network model and (ii) one or more design details attributed to the deep learning network model; automatically converting the intermediate representation into source code; and outputting the source code to at least one user.