Neural Network Parameter Constraint for Inference Efficiency
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Solution Overview
Problem
Conventional neural network training methods do not optimize network parameters and feature maps based on the architecture of inference platforms, leading to suboptimal performance and increased computational intensity during inference.
Innovation Solution
The method involves obtaining architecture constraints for the inference platform circuitry, training the neural network on a more performant training platform, and constraining network parameters and feature maps based on these constraints to ensure efficient usage of the inference platform during inference.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Productivity
If neural networks are trained without architecture constraints on inference platforms, then the training process is simpler and faster, but the computational intensity during inference increases and performance decreases
Solution Approach 1:
The patent applies preliminary action by incorporating architecture constraints during the training phase on the training platform. The training process proactively adapts network parameters and feature maps to match the inference platform's computational capabilities before deployment, rather than attempting optimization during inference. This preliminary adaptation reduces the computational burden during actual inference operations.
Solution Approach 2:
The patent utilizes parameter changes by modifying network parameters (weights, biases) and feature map characteristics during constrained training to optimize for the specific architecture of the inference platform. The training process adjusts these parameters to exploit the inference platform's computational strengths, thereby improving inference performance while reducing computational intensity.
2Productivity
If the training platform is more highly performant than the inference platform, then training can be completed faster, but the inference platform becomes underutilized and less power efficient
Solution Approach 1:
The patent applies preliminary action by performing architecture-aware optimization during the training phase on the high-performance training platform. This preliminary adaptation ensures that the trained model is specifically optimized for the target inference platform's architecture, enabling the inference platform to operate at full efficiency despite the performance gap between training and inference systems.
Solution Approach 2:
The patent introduces architecture constraints as an intermediary that bridges the gap between the high-performance training platform and the less performant inference platform. These constraints act as a mediator that translates the training capabilities into inference-optimized parameters, ensuring compatibility and efficient utilization of the inference platform's resources.
3Productivity
If architecture constraints are applied during training, then inference performance is optimized, but the training process becomes more complex
Solution Approach 1:
The patent applies universality by designing a constrained training framework that can accommodate different inference platform architectures through a unified approach. The same training methodology and constraint application process can be used across various platform types, making the solution broadly applicable despite the added complexity of architecture awareness.
Data Source
AI summary
An example a method of optimizing a neural network having a plurality of layers includes: obtaining an architecture constraint for circuitry of an inference platform that implements the neural network; training the neural network on a training platform to generate network parameters and feature maps for the plurality of layers; and constraining the network parameters, the feature maps, or both based on the architecture constraint.