Optimizing DNNs for Resource-Constrained Edge Environments

Kernekoncepter

Optimizing deep neural network architectures for resource-constrained edge environments while maintaining high accuracy.

Resumé

This paper proposes optimizing Deep Neural Networks (DNNs) to improve hardware utilization and enable on-device training in resource-constrained edge environments. Efficient parameter reduction strategies are implemented on Xception to reduce model size without sacrificing accuracy, decreasing memory usage during training. Two experiments, Caltech-101 image classification and PCB defect detection, show the optimized model outperforms Xception and lightweight models in test accuracy, memory usage, training, and inference times. Transfer learning benefits are observed with decreased memory usage. The optimized model architecture achieves Pareto optimality by balancing accuracy and low memory utilization objectives.

Statistik

Our model has a better test accuracy of 76.21% compared to Xception's 75.89%.
Average memory usage of our model is 847.9MB compared to Xception's 874.6MB.
MobileNetV2 has the least average memory usage at 849.4MB.
Our model has the best test accuracy of 90.30% in PCB defect detection compared to Xception's 88.10%.

Citater

"Our optimized model architecture satisfies both accuracy and low memory utilization objectives."
"Transfer learning shows benefits with decreased memory usage."
"The results demonstrate improved performance over original Xception and lightweight models."

Vigtigste indsigter udtrukket fra

Achieving Pareto Optimality using Efficient Parameter Reduction for DNNs in Resource-Constrained Edge Environment

by Atah Nuh Mih... kl. arxiv.org 03-19-2024

https://arxiv.org/pdf/2403.10569.pdf

Dybere Forespørgsler

How can neural architectural search procedures enhance the optimization process

Neural architectural search procedures can significantly enhance the optimization process by automating the exploration of different neural network architectures. These procedures use algorithms to search through a vast space of possible architectures, considering various design choices such as layer types, connections, and hyperparameters. By doing so, they can discover novel and efficient architectures that may not have been considered manually. This automated approach saves time and resources by efficiently navigating the design space to find optimal or near-optimal solutions.
Neural architectural search procedures also enable the discovery of architecture patterns that are well-suited for specific tasks or constraints. They can adapt to different objectives like accuracy, latency, or resource utilization while exploring diverse architectural designs. Additionally, these procedures allow for experimentation with unconventional architectures that may lead to breakthroughs in model performance.
Overall, neural architectural search procedures streamline the optimization process by leveraging computational power to explore a wide range of possibilities systematically. They offer a data-driven approach to designing neural networks tailored to specific requirements and constraints.

What are the implications of negative transfer learning on target task performance

Negative transfer learning occurs when knowledge gained from one task (source task) hinders performance on another task (target task). In machine learning contexts like deep learning models trained using transfer learning techniques, negative transfer learning can have detrimental effects on target task performance.
The implications of negative transfer learning on target task performance include:

Decreased Performance: The model's accuracy on the target task may decrease due to conflicting information learned during pre-training.

Overfitting: Negative transfer could lead to overfitting if irrelevant features from the source domain negatively impact generalization in the target domain.

Increased Complexity: Models might become more complex as they try to reconcile conflicting information between tasks.

Resource Consumption: Negative transfer could result in increased memory usage and longer training times as models struggle with contradictory knowledge.

To mitigate negative transfer learning effects:

Choose relevant source tasks closely related to the target domain.
Regularize models during fine-tuning stages.
Monitor model behavior during training for signs of negative transfers.

How can the concept of Pareto optimality be applied in other machine learning contexts

Pareto optimality is a concept widely applicable in various machine learning contexts beyond neural network optimization:

Hyperparameter Tuning: In hyperparameter optimization processes like Bayesian Optimization or Grid Search, Pareto optimality principles can guide researchers towards finding an optimal set of hyperparameters balancing multiple objectives such as accuracy and computational cost.

Feature Selection: When selecting features for machine learning models, Pareto optimality helps identify feature subsets that maximize predictive power while minimizing complexity and computation requirements.

Ensemble Learning: In ensemble methods like boosting or bagging where multiple models are combined for improved predictions, Pareto optimality ensures that each individual model contributes uniquely without redundancy or unnecessary complexity.

By applying Pareto optimality principles across these contexts in machine learning research and practice, practitioners can achieve more balanced solutions that optimize trade-offs between competing objectives effectively while enhancing overall model efficiency and effectiveness at scale."