Equivariant Convolutional Networks for Low-Power Edge Deployment
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Solution Overview
Problem
Implementing complex machine learning tasks on low-power devices, such as edge devices, is challenging due to power consumption, computational efficiency, and memory footprint, and the scarcity of labeled data limits the deployment of machine learning models in new domains.
Innovation Solution
Utilizing equivariant convolutional neural networks (G-CNNs) with an equivariance loss term and data quantization techniques to enhance computational efficiency and data efficiency, enabling deployment on low-powered devices.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If complex machine learning models are deployed on low-power devices, then model performance and capability are improved, but power consumption and computational burden increase
Solution Approach 1:
The patent applies parameter changes by modifying the training process through equivariance loss functions that enforce symmetry constraints on the model. This changes the optimization landscape and convergence behavior, enabling the model to achieve better performance with fewer parameters and less computational resources, making it suitable for deployment on low-power devices.
Solution Approach 2:
The patent implements preliminary action by pre-processing the training data through symmetry augmentation and incorporating equivariance constraints into the loss function before model deployment. This preliminary enforcement of symmetry principles during training reduces the computational complexity required during inference, allowing efficient deployment on resource-constrained devices.
2Measurement precision
If larger neural network architectures are used to improve performance, then model accuracy is improved, but memory footprint and computational requirements increase
Solution Approach 1:
The patent changes the training parameters by incorporating equivariance loss functions that constrain the model to learn symmetry-preserving representations. This parameter modification enables the model to achieve high accuracy with simpler architectures by leveraging the inductive bias provided by symmetry constraints, reducing the need for complex and memory-intensive network designs.
Solution Approach 2:
The patent applies segmentation by decomposing the learning task into components that respect symmetry operations. By training the model to satisfy equivariance constraints for different symmetry transformations separately, the complex learning problem is broken down into more manageable sub-tasks, allowing simpler architectures to achieve comparable or better performance.
3Measurement precision
If more labeled data is collected to improve model performance, then training accuracy is improved, but data collection time and cost increase
Solution Approach 1:
The patent implements preliminary action by pre-processing the training data through symmetry augmentation, where existing labeled data is transformed to generate additional training examples that automatically satisfy equivariance constraints. This preliminary data preparation reduces the amount of raw labeled data needed, as the model learns from augmented samples that encode symmetry relationships, thereby reducing data collection time and cost.
4Productivity
If equivariance constraints are enforced during training, then data efficiency is improved, but training computational overhead increases
Solution Approach 1:
The patent changes the loss function parameters by adding equivariance constraint terms with carefully selected weighting coefficients. This parameter adjustment balances the trade-off between enforcing symmetry constraints and maintaining training efficiency, allowing the model to achieve improved data efficiency without excessive computational overhead during the training phase.
Data Source
AI summary
Certain aspects of the present disclosure provide a method of performing machine learning, comprising: generating a neural network model; and training the neural network model for a task with a first set of input data, wherein: the training uses a total loss function total including an equivariance loss component equivarnace according to total=task+αequivarnace, and α>0.