Binary Neural Network Architecture Search via Gradient Descent
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Solution Overview
Problem
Current neural architecture search (NAS) methods for binary neural networks are inefficient due to reliance on reinforcement learning, computational resource intensity, and lack of optimization for binary networks, leading to approximation errors and vanishing gradient problems.
Innovation Solution
The method employs an optimized search space, zeroise layers, diversity parameters, and inter-cell skip connections to perform NAS, allowing for efficient generation of binary neural networks with reduced flaws and improved accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Extent of automation
If reinforcement learning is used for neural architecture search, then the neural network can be automatically designed, but the search process takes too long and requires excessive computing resources
Solution Approach 1:
The patent replaces the reinforcement learning-based search mechanism with a differentiable architecture search method using gradient descent. Instead of using complex RL agents to explore the search space, the invention formulates architecture search as an optimization problem where architecture parameters are continuously relaxed and optimized through gradient-based methods, dramatically reducing search time and computational requirements
Solution Approach 2:
The invention changes the parameter representation by relaxing discrete architecture parameters into continuous domains. This allows the use of gradient descent for optimization instead of reinforcement learning, enabling efficient and automated neural network architecture design without the excessive computational burden of RL-based methods
2Extent of automation
If reinforcement learning is used for neural architecture search, then the neural network can be automatically designed, but excessive computing resources are required
Solution Approach 1:
The patent substitutes the computationally intensive reinforcement learning system with a gradient-based optimization approach. By formulating architecture search as a differentiable optimization problem, the method reduces computing resource requirements while maintaining automated design capability
Solution Approach 2:
The invention uses a supernet that copies and shares weights across multiple possible architecture configurations. This allows gradient information to be efficiently propagated through all potential architectures simultaneously, reducing the computational resources needed compared to evaluating each architecture separately through reinforcement learning
3Ease of manufacture
If direct approximation of floating point neural networks is used for binary neural networks, then the design process is simplified, but approximation errors and quantization errors increase
Solution Approach 1:
The patent performs preliminary action by training the supernet in floating-point precision first, then applying quantization to obtain binary neural networks. This preliminary training in higher precision ensures that the network learns optimal architecture and parameters before binary conversion, minimizing approximation and quantization errors while keeping the design process manageable
Solution Approach 2:
The invention cushions against quantization errors by training with floating-point precision beforehand and using knowledge distillation from the floating-point supernet to guide the binary network training. This preliminary high-precision training creates a robust foundation that tolerates the subsequent binary quantization better
4Device complexity
If conventional NAS procedures optimized for floating point neural networks are applied to binary neural networks, then the search process can be simplified, but the suitability and performance for binary networks deteriorates
Solution Approach 1:
The patent applies local quality by designing a search space and training procedure specifically tailored for binary neural networks. The supernet architecture, loss functions, and training strategies are locally optimized for binary constraints, ensuring both simplicity and high suitability for binary network generation
Solution Approach 2:
The invention changes the parameter optimization approach to accommodate binary constraints. By using differentiable architecture search with gradient descent and implementing proper quantization strategies, the method maintains simple search procedures while ensuring high reliability and suitability for binary neural networks
Data Source
AI summary
A method for generating a target network by performing neural architecture search using optimized search space is provided. The method includes steps of: a computing device (a) if a target data is inputted into the target network, allowing the target network to apply neural network operation to the target data, to generate an estimated search vector; and (b) allowing a loss layer to calculate architecture parameter losses by referring to the estimated search vector and a ground truth search vector, and to perform backpropagation by referring to the architecture parameter losses to update architecture parameter vectors for determining final layer operations among candidate layer operations included in an optimized layer type set corresponding to the optimized search space and wherein the final layer operations are to be performed by neural blocks, within cells of the target network, arranged according to an optimized cell template corresponding to the optimized search space.