Neural Network Compound Scaling for Accuracy-Cost Balance
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Solution Overview
Problem
Conventional methods for scaling neural networks are inefficient and computationally expensive due to the need for extensive hyper-parameter tuning, often resulting in low accuracy and inefficiency when increasing network width, depth, or resolution.
Innovation Solution
A system that determines a final neural network architecture by uniformly scaling network width, depth, and resolution using a compound coefficient, avoiding hyper-parameter tuning and optimizing resource usage to maintain model efficiency and accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If network width, depth, or resolution is increased to improve accuracy, then model accuracy is improved, but computational cost and resource usage increase significantly
Solution Approach 1:
The patent applies parameter changes by systematically adjusting network width, depth, and resolution parameters simultaneously using a compound coefficient. This allows the model to achieve improved accuracy while controlling computational cost through coordinated parameter modification rather than arbitrary increases in any single parameter.
Solution Approach 2:
The patent implements dynamics by introducing a dynamic scaling approach where network parameters are adjusted based on available computational resources. The compound coefficient enables flexible adaptation of network architecture to match resource constraints, allowing the model to optimize the balance between accuracy and computational cost.
2Measurement precision
If network architecture is scaled up arbitrarily to improve accuracy, then model capacity increases, but training efficiency decreases due to extensive hyper-parameter tuning required
Solution Approach 1:
The patent applies preliminary action by pre-defining the relationship between network parameters through the compound coefficient formula before training begins. This preliminary formulation of the scaling relationship eliminates the need for extensive hyper-parameter tuning during the scaling process, thereby improving training efficiency while maintaining model accuracy.
Solution Approach 2:
The patent systematically changes parameters by applying the compound coefficient to simultaneously adjust network width, depth, and resolution. This coordinated parameter transformation approach replaces arbitrary scaling and reduces the computational overhead associated with hyper-parameter optimization.
3Measurement precision
If network parameters are increased to create larger models, then model accuracy improves, but the number of parameters and operations increases making training and inference slower
Solution Approach 1:
The patent applies parameter changes by using the compound coefficient to coordinate adjustments in network width, depth, and resolution. This ensures that accuracy improvements are achieved through balanced parameter scaling rather than indiscriminate increases, thereby controlling the growth of parameter count and operational complexity to minimize training and inference time.
Data Source
AI summary
A method for determining a final architecture for a neural network to perform a particular machine learning task is described. The method includes receiving a baseline architecture for the neural network, wherein the baseline architecture has a network width dimension, a network depth dimension, and a resolution dimension; receiving data defining a compound coefficient that controls extra computational resources used for scaling the baseline architecture; performing a search to determine a baseline width, depth and resolution coefficient that specify how to assign the extra computational resources to the network width, depth and resolution dimensions of the baseline architecture, respectively; determining a width, depth and resolution coefficient based on the baseline width, depth, and resolution coefficient and the compound coefficient; and generating the final architecture that scales the network width, network depth, and resolution dimensions of the baseline architecture based on the corresponding width, depth, and resolution coefficients.