Augmented Neural Network Dynamic Configuration
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Solution Overview
Problem
Conventional vision transformer systems struggle with unpredictable execution times in real-time applications, leading to potential missed events, such as a car entering a frame in autonomous driving, due to fixed inference times and inability to adapt to varying loads.
Innovation Solution
A dynamically configurable augmented neural network model that adapts to performance constraints like inference execution time, throughput, and energy consumption without requiring retraining, using configurable augmentations within the original model to select optimal execution paths and reduce computations.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If the system increases total capability to handle worst-case load, then reliability is improved, but device complexity and cost increase
Solution Approach 1:
The patent implements dynamic configuration of neural network models at runtime based on current system load and performance constraints. The system transitions from static model deployment to dynamic model selection and configuration, allowing the same hardware to adapt its processing capability to match actual workload demands, thereby achieving high reliability without over-provisioning for worst-case scenarios.
Solution Approach 2:
The system changes operational parameters (model configuration, precision, batch size) dynamically based on performance constraints. By adjusting these parameters at runtime rather than fixing them in advance, the system can handle varying loads efficiently, maintaining reliability across different operating conditions without increasing hardware complexity.
2Productivity
If frames are dropped to ensure inference completion, then productivity is improved, but reliability deteriorates
Solution Approach 1:
The system applies partial computation strategies where neural network models are configured to perform reduced computations when time is constrained, rather than completely dropping frames. This allows the system to process more frames than would be possible with full computation, improving throughput while still maintaining acceptable accuracy for critical event detection.
Solution Approach 2:
The system dynamically adjusts the level of computation performed on each frame based on timing constraints and detected importance. Rather than uniformly dropping frames or processing all frames at full complexity, the system adapts its processing strategy in real-time to balance throughput and reliability.
3Adaptability or versatility
If multiple pruned neural network versions are constructed for different performance constraints, then adaptability is improved, but device complexity increases
Solution Approach 1:
The patent creates a single universal neural network model that can operate under multiple performance constraints through dynamic configuration. Rather than maintaining separate pruned models for different constraints, this universal model can adapt its behavior and computation level based on the current performance requirements, reducing model management complexity while maintaining adaptability.
Solution Approach 2:
The system implements dynamic model configuration where a single neural network model can be adjusted at runtime to meet different performance constraints. This dynamic approach replaces the static strategy of maintaining multiple fixed models, allowing the system to adapt to varying constraints without increasing the number of models that must be stored and managed.
4Reliability
If the neural network is designed for worst-case operating conditions, then reliability is improved, but productivity deteriorates
Solution Approach 1:
The system transitions from static worst-case design to dynamic adaptation. The neural network model and its configuration are adjusted in real-time based on actual operating conditions, allowing the system to operate at high efficiency during normal conditions while maintaining the capability to meet reliability requirements when constraints are tight, rather than always operating at worst-case performance levels.
Solution Approach 2:
The system changes operational parameters dynamically based on actual performance constraints rather than designing for fixed worst-case parameters. This allows the system to optimize its performance for current conditions, achieving high productivity during favorable conditions while maintaining reliability safeguards when constraints are violated.
Data Source
AI summary
A neural network model is augmented for dynamic configuration and execution in real-time according to performance constraints. In an embodiment, the neural network model is a transformer neural network model. The performance constraints may include a metric, such as inferencing execution time or energy consumption and a target value for the metric. The augmented neural network model is characterized for various configurations and settings are determined corresponding to a variety of the performance constraints. One or more performance constraints may be provided as an input to dynamically select a configuration of the augmented neural network model. Through dynamic configuration, the augmented neural network model may adapt to real-time changes in the performance constraints. However, the trained weights for an original (before augmentation) neural network model may be used by the augmented neural network model without modification.