Time-Aware Inference in Deep Neural Networks
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Solution Overview
Problem
Existing AI systems face challenges in managing computational costs and time-critical decisions, as they incur high costs with delayed actions, necessitating the need for flexible inference procedures that can optimize net value by controlling computational complexity and accuracy.
Innovation Solution
The implementation of a time-constrained inference scheme using Learning Kernel-Activation Modules (LKAMs) within deep neural networks, which allows for dynamic adjustment of computational complexity by selectively activating convolutional kernels, enabling control over inference time and accuracy through a system that can alter configurations on-the-fly during computation.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Measurement precision
If deep neural networks compute with additional precision, then the value of action increases, but the cost of computation increases linearly with delay
Solution Approach 1:
The patent implements dynamic computational complexity adjustment by introducing a time-constrained inference scheme that modifies the neural network's computational behavior based on available time. The system dynamically selects which neural processing elements to activate and adjusts the depth of network layers processed, allowing the computational complexity to adapt in real-time to meet deadlines while maintaining optimal accuracy within the time budget.
Solution Approach 2:
The system changes key parameters including the activation threshold of neural processing elements, the depth of network layers processed, and the number of computation iterations. By adjusting these parameters based on time constraints, the system can trade off between computation speed and accuracy, achieving optimal performance for time-critical applications.
2Measurement precision
If the neural network increases computational complexity to improve accuracy, then the quality of results improves, but the inference time increases
Solution Approach 1:
The patent segments the neural network into multiple processing stages with variable depth. Instead of processing all layers uniformly, the system divides the network into shallow and deep processing paths, allowing selective activation of computational stages based on time constraints. This segmentation enables the system to achieve sufficient accuracy with fewer layers when time is limited, while maintaining high accuracy options when more time is available.
Solution Approach 2:
The system implements partial action by processing only the necessary portion of the neural network required to meet the time constraint and accuracy threshold. Rather than always executing the full network depth, the system performs partial inference by activating only the required number of layers and neural processing elements, reducing unnecessary computation while maintaining adequate performance.
3Reliability
If the system activates more neural processing elements, then the computational capacity increases, but the resource consumption increases
Solution Approach 1:
The patent applies local quality by differentiating the activation state of individual neural processing elements based on their specific function and the current time constraint. Rather than uniformly activating all processing elements, the system selectively activates only those neurons and layers that are most critical for the current inference task and time budget, optimizing the balance between reliability and resource consumption at the local level of individual computational units.
Data Source
AI summary
An exemplary aspect relates to the field of pattern recognition, and in one exemplary embodiment to the field of image recognition. More specifically, an embodiment relates to the use of deep neural networks for image recognition and how these kinds of pattern classification structures may be augmented in order to become aware of the available computation time, and the available computational resources so as to appropriately adjust the computational complexity of their associated algorithms and consequently their need for computing resources. The methods and systems described herein at least enable more economical and flexible implementations for porting to embedded computing frameworks by respecting their computational resources.