Neural Network Selector Adapting Architecture to Memory Constraints
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Solution Overview
Problem
Current neural network processing systems face challenges in efficiently training and deploying deep learning models for tasks like image segmentation, particularly in resource-constrained environments such as autonomous vehicles, where real-time processing and adaptability are crucial.
Innovation Solution
The development of specialized inference and training logic within computing systems, including dedicated hardware and software components, to optimize neural network operations, such as using TensorFlow Processing Units or Nervana processors, which manage weight parameters and data storage efficiently for forward and backward propagation, enabling efficient training and deployment of neural networks.
Engineering Contradictions & Design Principles
Engineering Contradiction Analysis
1Reliability
If larger and more complex neural networks are used to improve performance for tasks like image segmentation, then accuracy and capability increase, but computational resources and memory requirements increase
Solution Approach 1:
The system dynamically selects neural network architectures based on available memory resources. The neural network selector evaluates memory constraints and adapts the network complexity accordingly, transitioning between different network configurations to balance performance and resource consumption
Solution Approach 2:
The patent changes architectural parameters of neural networks (such as number of layers, filters, or computational complexity) based on memory availability. By adjusting these parameters dynamically, the system optimizes the trade-off between model performance and computational resource requirements
2Reliability
If more sophisticated neural network architectures are deployed to enhance machine learning capabilities, then task accuracy improves, but device complexity increases
Solution Approach 1:
The neural network selector provides dynamic adaptation by choosing appropriate network architectures based on the host device's memory characteristics. This dynamic selection simplifies the system by avoiding deployment of overly complex networks on resource-constrained devices
Solution Approach 2:
The system creates a universal neural network selector that can operate across different device types and memory configurations. This multi-functional selector handles various neural network types (convolutional, recurrent, transformers) and adapts them to different computational environments, reducing overall system complexity
3Speed
If real-time processing is implemented in resource-constrained environments like autonomous vehicles, then responsiveness improves, but available computational memory decreases
Solution Approach 1:
The system dynamically adjusts neural network complexity based on real-time memory availability in resource-constrained environments. The neural network selector continuously monitors memory resources and adapts network architecture to maintain real-time processing capability while respecting memory limits
Solution Approach 2:
The system employs partial neural networks or simplified architectures when memory is constrained, using only the necessary computational components required for real-time tasks rather than deploying full complex models, thus achieving real-time processing within memory limitations
Data Source
AI summary
Apparatuses, systems, and techniques to select a neural network using an amount of memory to be used. In at least one embodiment, a processor includes one or more circuits to cause one or more neural networks to be selected from a plurality of neural networks based, at least in part, on an amount of memory to be used by the one oe more neural networks.