Dynamic Early Exit Neural Network Inference
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Solution Overview
Problem
Deep neural network (DNN) inference is computationally expensive, and existing early exit techniques rely on manual configurations that lack adaptivity to target datasets, leading to inefficient inference times and increased memory usage.
Innovation Solution
Dynamic selection of exit points in DNN models based on target datasets using statistics such as KL Divergence, Cross Entropy, and output changes, with a system that determines optimal exit points and thresholds to reduce inference time while maintaining accuracy.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Device complexity
If manual early exit configurations are used, then device complexity is reduced, but adaptability to target datasets deteriorates
Solution Approach 1:
The patent implements dynamic early exit selection where the system automatically determines optimal exit points based on the target dataset characteristics. Instead of fixed manual configurations, the system dynamically adapts exit point selections to match specific dataset requirements, resolving the contradiction between simplicity and adaptability.
Solution Approach 2:
The system performs self-configuration by automatically identifying optimal early exit points for given target datasets without requiring manual intervention. The methodology enables the model to serve itself by adapting its inference structure to the specific characteristics of each dataset it encounters.
2Measurement precision
If full neural network processing is used, then measurement precision is maintained, but loss of time increases
Solution Approach 1:
The patent applies partial action by allowing the neural network to process inputs only to the extent necessary. By implementing early exit mechanisms, the system performs partial processing for samples that can be confidently classified earlier in the network, reducing inference time while maintaining accuracy for samples that require full processing.
Solution Approach 2:
The neural network is segmented into multiple processing stages with potential exit points at different depths. This segmentation allows the system to divide inference into optional stages, enabling early termination for simple cases while preserving the option to proceed through all stages for complex cases requiring higher precision.
3Productivity
If early exit points are added to neural network, then productivity is improved, but device complexity increases
Solution Approach 1:
The patent implements multi-functionality by designing exit points that serve dual purposes: they act as both processing stages in the main network flow and as potential termination points for early exit. This universal design allows the same network structure to function in multiple modes (full processing or early exit) without requiring separate dedicated structures for each function.
4Ease of operation
If manual exit configurations are used, then ease of operation is maintained, but adaptability to different datasets deteriorates
Solution Approach 1:
The system eliminates the need for manual configuration by implementing self-service functionality. The methodology automatically analyzes target dataset characteristics and configures optimal early exit points without user intervention, thereby maintaining ease of operation while dramatically improving dataset-specific adaptability.
Data Source
AI summary
Methods, systems, and computer program products for accelerating inference of neural network models via dynamic early exits are provided herein. A computer-implemented method includes determining a plurality of candidate exit points of a neural network model; obtaining a plurality of outputs of the neural network model for data samples in a target dataset, wherein the plurality of outputs comprises early outputs of the neural network model from the plurality of candidate exit points and regular outputs of the neural network model; and a set of one or more exit points from the plurality of candidate exits points that are dependent on the target dataset based at least in part on the plurality of outputs.