Energy-Efficient and Uncertainty-Aware Biomass Composition Prediction on Resource-Constrained Edge Devices

Grunnleggende konsepter

A hybrid approach that leverages both pruned and unpruned deep learning models to enable energy-efficient and accurate biomass composition prediction on resource-constrained edge devices.

Sammendrag

The paper proposes a hybrid approach to enable energy-efficient and accurate biomass composition prediction on resource-constrained edge devices. The key insights are:

1. Applying filter pruning at initialization to reduce the energy consumption of deep learning models for biomass estimation, while observing a performance drop on challenging images.

2. Training the pruned models to predict a probability distribution over biomass values, where the variance of the distribution is positively correlated with the prediction error. This allows identifying harder images that require re-inference using the more accurate but energy-intensive unpruned model.

3. Evaluating the proposed hybrid approach on two biomass estimation datasets (GrassClover and Irish clover) using ResNet18 and VGG16 architectures. The results show that the hybrid approach can reduce energy consumption by 40-60% compared to the unpruned model, while maintaining a comparable accuracy.

4. Demonstrating the real-world energy efficiency of the hybrid approach on a NVIDIA Jetson Nano edge device, where the energy consumption is significantly reduced without compromising much on the prediction accuracy.

Statistikk

The paper reports the following key statistics:

On the Irish clover dataset, the pruned ResNet18 model at 80% pruning rate achieves an RMSE of 5.40 on phone images, compared to 4.81 for the unpruned model.
On the GrassClover dataset, the pruned ResNet18 model at 80% pruning rate achieves an RMSE of 11.21, compared to 10.06 for the unpruned model.
On the NVIDIA Jetson Nano edge device, the proposed hybrid approach reduces energy consumption by 40-60% compared to the unpruned model.

Sitater

"We find energy-aware pruning at initialization attractive for biomass estimation because although prior studies demonstrate the accuracy of deep learning models to solve the task, these large models are intended for deployment on energy-constrained edge devices like smartphones."
"We find that pruned models manage to maintain good accuracy results on the higher quality camera images despite the compression but that the performance drops on the more challenging camera images."
"We observe that this hybrid approach only slightly increases the error rate of previous algorithms while largely reducing the energy requirements of deep learning biomass estimation algorithms."

Viktige innsikter hentet fra

Energy-Efficient Uncertainty-Aware Biomass Composition Prediction at the Edge

by Muhammad Zaw... klokken arxiv.org 04-18-2024

https://arxiv.org/pdf/2404.11230.pdf

Dypere Spørsmål

How can the proposed hybrid approach be extended to other computer vision tasks beyond biomass estimation

The proposed hybrid approach, which combines pruned and unpruned models based on prediction uncertainty, can be extended to various other computer vision tasks beyond biomass estimation. One way to extend this approach is to apply it to tasks such as object detection, image segmentation, and facial recognition. For object detection, the hybrid model can be used to improve accuracy on challenging images with occlusions or complex backgrounds. In image segmentation tasks, the uncertainty-guided approach can help refine boundaries between different classes or objects. Additionally, in facial recognition, the hybrid model can enhance accuracy on images with varying lighting conditions or facial expressions. By incorporating uncertainty estimation into the decision-making process, the model can dynamically adjust its predictions based on the confidence level, leading to more robust and reliable results across different computer vision applications.

What are the potential limitations of using variance as a proxy for prediction confidence, and how can this be further improved

While using variance as a proxy for prediction confidence is a valuable approach, there are potential limitations that need to be considered. One limitation is that variance may not always accurately reflect the true uncertainty in the predictions, especially in cases where the model is overconfident or underconfident. To address this limitation, additional uncertainty estimation techniques such as Bayesian neural networks or ensemble methods can be explored to provide a more comprehensive understanding of prediction uncertainty. These methods can capture different sources of uncertainty, including model uncertainty and data uncertainty, leading to more reliable confidence estimates. Furthermore, incorporating calibration techniques to adjust the predicted variance can help improve the accuracy of uncertainty estimates. By calibrating the uncertainty estimates, the model can better reflect the true confidence level in its predictions, enhancing the overall performance of the hybrid approach.

How can the energy-efficiency and accuracy trade-off be dynamically adjusted based on the end-user's requirements or the device's battery level

To dynamically adjust the energy-efficiency and accuracy trade-off based on end-user requirements or device battery level, adaptive strategies can be implemented within the hybrid approach. One approach is to introduce a feedback mechanism that continuously monitors the device's battery level and performance metrics. Based on this feedback, the model can dynamically adjust its operating mode, switching between the pruned and unpruned models to optimize energy consumption while maintaining accuracy. Additionally, incorporating user-defined thresholds for energy consumption and accuracy levels can allow users to customize the trade-off according to their specific needs. By providing flexibility in adjusting the energy-efficiency and accuracy balance, the hybrid approach can adapt to varying conditions and requirements, ensuring optimal performance in different scenarios.