Neural Network Generation via Differentiable Architecture Search
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Solution Overview
Problem
Existing neural network generation methods face inefficiencies in terms of memory and computing resource utilization, leading to suboptimal performance in tasks that rely on these networks.
Innovation Solution
The implementation of a neural architecture search (NAS) algorithm, specifically the Differentiable Architecture Search with Transformer (DAST) algorithm, which optimizes neural network models by searching a global structure and local operations within constraints such as memory consumption, and visualizes long-range dependencies through attention matrices, to efficiently generate neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If traditional neural network generation methods are used, then the process is simpler, but memory usage increases and performance deteriorates
Solution Approach 1:
The patent applies parameter changes by transforming the discrete architecture search problem into a continuous differentiable space, allowing gradient-based optimization. This enables the system to find optimal neural network architectures that balance memory consumption and performance by continuously adjusting architectural parameters rather than searching through discrete configurations.
Solution Approach 2:
The patent introduces an intermediary differentiable architecture search mechanism that mediates between the discrete neural network architecture space and continuous optimization methods. This intermediary layer enables efficient exploration of architectural configurations while maintaining control over memory consumption constraints.
2Productivity
If traditional neural network generation methods are used, then the implementation is simpler, but computing resource utilization increases and efficiency decreases
Solution Approach 1:
The patent transforms the computing resource optimization problem by changing the optimization landscape from discrete to continuous, enabling more efficient gradient-based search. This parameter transformation allows the system to find optimal architectures with better processing efficiency while reducing unnecessary computing resource consumption through directed optimization rather than exhaustive search.
3Reliability
If neural networks are optimized for performance, then task execution improves, but memory and computing resource requirements increase
Solution Approach 1:
The patent resolves this contradiction by changing the optimization parameters to include explicit constraints on memory and computing resources alongside performance metrics. The differentiable architecture search framework allows simultaneous optimization of multiple parameters, finding architectures that achieve high task execution performance while maintaining controlled resource requirements through balanced parameter adjustment.
Data Source
AI summary
Apparatuses, systems, and techniques to generate one or more neural networks. In at least one embodiment, one or more neural networks are generated, based on, for example, one or more convolutional neural network operations and one or more transformer neural network operations.