Neural Network Early-Exit Thresholds for Accurate Fast Inference
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Solution Overview
Problem
The computational expense of processing data through full neural networks, particularly those with many parameters, can be reduced by tapping intermediate results from early-exit points, which are approximations of the final result, but ensuring these approximations meet certain confidence thresholds to avoid unnecessary full processing.
Innovation Solution
A method to determine when neural network processing can be cut short by using early-exit outputs by setting thresholds based on confidence levels and statistical guarantees, ensuring the early-exit outputs meet predetermined conditions.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If full neural network processing is used, then processing accuracy is maintained, but computational expense and processing time increase
Solution Approach 1:
The neural network is divided into multiple stages with intermediate exit points. The network processes data through sequential layers, allowing early exit at any stage where sufficient confidence is achieved. This segmentation enables partial processing when early stages provide adequate results, reducing overall processing time while maintaining accuracy through the layered structure that preserves computational depth when needed.
Solution Approach 2:
The system performs partial processing by exiting the network early when confidence thresholds are met, rather than completing all processing stages. This partial action approach allows the system to achieve sufficient accuracy without always executing the full computational sequence, thereby reducing processing time for cases where early exit is appropriate while maintaining the option for full processing when necessary.
2Productivity
If early-exit points are used to reduce computational expense, then processing speed improves, but confidence in results may decrease
Solution Approach 1:
The system incorporates confidence scores from each neural network layer as feedback signals. At each early-exit point, the confidence score generated by that layer is evaluated against predefined thresholds. This feedback mechanism allows the system to dynamically decide whether to exit early based on the quality of the current processing stage, ensuring that only exits with sufficient confidence are accepted, thus maintaining result reliability while enabling speedup when appropriate.
Solution Approach 2:
The system changes the parameter of processing depth dynamically based on confidence thresholds. When confidence scores at early-exit points meet the thresholds, the system terminates processing early; when thresholds are not met, processing continues to deeper layers. This parameter change approach allows the system to adapt the processing depth to the specific input data, balancing processing speed and result confidence by selecting the appropriate exit point based on real-time confidence evaluation.
3Productivity
If thresholds for early exit are set low, then more records can be processed quickly, but accuracy of early-exit outputs decreases
Solution Approach 1:
The system dynamically adjusts the threshold parameter based on the specific requirements of each record or batch of records. By changing threshold values adaptively, the system can optimize the balance between processing speed and accuracy for different datasets or task requirements, allowing low thresholds for high-speed processing when accuracy requirements are relaxed, and high thresholds for high-accuracy processing when speed is less critical.
Data Source
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AI summary
A method (100) for determining for which inputs records (2) of measurement data the processing by a neural network (1) may be cut short by obtaining the output (3, 3a-3c) from an early-exit point (1a-1c) of the neural network (1), rather than by traversing the whole neural network (1), the method comprising the steps of: • providing (110) a set of calibration records (2*) of measurement data; • processing (120) the calibration records (2*) of measurement data by the full neural network (1) to obtain reference outputs (3*); • recording (130) one or more early-exit outputs (3a*-3c*) that the neural network (1) outputs for the calibration records (2*) at one or more early-exit points (1a-1c), as well as respective confidences (4a*-4c*) of these early-exit outputs (3a*-3c*); • providing (140) a set of predetermined conditions (5) that are each dependent both on early-exit outputs (3a*-3c*) and on reference outputs (3*); and • evaluating (150) one or more thresholds (6) for the confidences (4a-4c) of the early-exit outputs (3a-3c) such that, if the confidences (4a-4c) exceed the thresholds (6), the respective early-exit outputs (3a-3c) can be expected to meet the conditions (5).