arXiv:2404.07217v1 Announce Type: cross Abstract: We propose a communication-efficient collaborative inference framework in the domain of edge inference, focusing on the efficient use of vision transformer (ViTs) models. The partitioning strategy of conventional collaborative inference fails to reduce communication cost because of the inherent architecture of ViTs maintaining consistent layer dimensions across the entire transformer encoder. Therefore, instead of employing the partitioning strategy, our framework utilizes a lightweight ViT model on the edge device, with the server deploying a complicated ViT model. To enhance communication efficiency and achieve the classification accuracy of the server model, we propose two strategies: 1) attention-aware patch selection and 2) entropy-aware image transmission. Attention-aware patch selection leverages the attention scores generated by the edge device’s transformer encoder to identify and select the image patches critical for classification. This strategy enables the edge device to transmit only the essential patches to the server, significantly improving communication efficiency. Entropy-aware image transmission uses min-entropy as a metric to accurately determine whether to depend on the lightweight model on the edge device or to request the inference from the server model. In our framework, the lightweight ViT model on the edge device acts as a semantic encoder, efficiently identifying and selecting the crucial image information required for the classification task. Our experiments demonstrate that the proposed collaborative inference framework can reduce communication overhead by 68% with only a minimal loss in accuracy compared to the server model.
Title: Enhancing Communication Efficiency in Edge Inference: A Collaborative Framework with Vision Transformers
Introduction:
In the era of edge computing, where data processing is increasingly shifting towards the edge devices, efficient communication becomes crucial for collaborative inference. This article presents a novel framework that focuses on the efficient utilization of vision transformer (ViT) models in the domain of edge inference. Unlike conventional partitioning strategies, which fail to reduce communication costs due to the consistent layer dimensions of ViTs, this framework adopts a different approach.
The proposed framework leverages a lightweight ViT model on the edge device, while deploying a more complex ViT model on the server. The goal is to enhance communication efficiency while maintaining the classification accuracy of the server model. To achieve this, two key strategies are introduced: attention-aware patch selection and entropy-aware image transmission.
Attention-aware patch selection utilizes the attention scores generated by the edge device’s transformer encoder to identify and select the image patches crucial for accurate classification. By transmitting only the essential patches to the server, this strategy significantly improves communication efficiency.
Furthermore, entropy-aware image transmission employs min-entropy as a metric to determine whether to rely on the lightweight model on the edge device or request inference from the server model. This approach ensures that the most appropriate model is utilized based on the complexity and uncertainty of the image data.
In this collaborative inference framework, the lightweight ViT model on the edge device acts as a semantic encoder, efficiently identifying and selecting the crucial image information required for the classification task. Experimental results demonstrate that the proposed framework reduces communication overhead by 68% with only a minimal loss in accuracy compared to the server model.
By addressing the challenges of communication efficiency in edge inference and leveraging the power of vision transformers, this framework presents a promising solution for optimizing collaborative inference in edge computing environments.
An Innovative Approach to Communication-Efficient Collaborative Inference in Edge Computing
Edge computing has gained significant attention in recent years, enabling faster processing and lower latency for various applications. One key challenge in edge computing is achieving efficient communication between edge devices and central servers. To tackle this challenge, we propose a communication-efficient collaborative inference framework that focuses on the efficient use of vision transformer (ViTs) models.
Conventional collaborative inference strategies often rely on partitioning the model across edge devices and servers. However, this approach fails to reduce communication cost effectively for ViT models due to the consistent layer dimensions maintained across the entire transformer encoder architecture. Instead, our framework takes a different approach by leveraging a lightweight ViT model on the edge device and deploying a more complex ViT model on the server.
Attention-Aware Patch Selection
One of the key strategies in our framework is attention-aware patch selection. This approach utilizes the attention scores generated by the edge device’s transformer encoder to identify and select the image patches critical for classification. By leveraging the inherent attention mechanism in ViT models, the edge device can transmit only the essential patches to the server, significantly improving communication efficiency.
Entropy-Aware Image Transmission
In addition to attention-aware patch selection, we propose entropy-aware image transmission as another strategy to enhance communication efficiency. This strategy uses min-entropy as a metric to determine whether to rely on the lightweight model on the edge device or request inference from the server model. By accurately assessing the information content of an image, the framework can make an informed decision on which model to utilize, minimizing unnecessary communication overhead.
In our framework, the lightweight ViT model on the edge device acts as a semantic encoder, efficiently identifying and selecting the crucial image information required for the classification task. By combining attention-aware patch selection and entropy-aware image transmission, our proposed collaborative inference framework achieves a balance between communication efficiency and classification accuracy.
Experimental results demonstrate the effectiveness of our framework in reducing communication overhead. Our framework achieves a remarkable 68% reduction in communication cost compared to the conventional partitioning strategy, with only minimal loss in accuracy when compared to the server model. These results showcase the potential of our approach in enabling efficient edge inference in various domains.
“Our communication-efficient collaborative inference framework leverages the strengths of vision transformer models while addressing the challenges of communication overhead in edge computing. By introducing attention-aware patch selection and entropy-aware image transmission, we provide innovative solutions for reducing communication cost without compromising classification accuracy. This framework holds great promise for practical implementation in edge computing scenarios.” – Research Team
The paper titled “A Communication-Efficient Collaborative Inference Framework for Edge Inference with Vision Transformers” addresses the challenge of reducing communication costs in collaborative inference for edge devices using vision transformer (ViT) models. Collaborative inference involves distributing the computational load between edge devices and a central server to improve efficiency and reduce latency.
The authors argue that the conventional partitioning strategy used in collaborative inference is not effective for ViT models due to their consistent layer dimensions across the entire transformer encoder. Instead, they propose a framework that utilizes a lightweight ViT model on the edge device and a more complex ViT model on the server. This approach aims to maintain classification accuracy while enhancing communication efficiency.
To achieve this, the authors introduce two strategies: attention-aware patch selection and entropy-aware image transmission. The attention-aware patch selection strategy utilizes attention scores generated by the edge device’s transformer encoder to identify and select image patches crucial for classification. By transmitting only the essential patches to the server, the framework significantly improves communication efficiency.
The entropy-aware image transmission strategy utilizes min-entropy as a metric to determine whether to rely on the lightweight model on the edge device or request inference from the server model. This strategy ensures that the edge device makes an informed decision on when to offload the inference task to the server, based on the complexity and uncertainty of the input image.
The experiments conducted by the authors demonstrate that their proposed collaborative inference framework can reduce communication overhead by 68% while maintaining a minimal loss in accuracy compared to the server model. This suggests that their approach effectively balances communication efficiency and classification performance.
Overall, this paper contributes to the field of collaborative inference for edge devices by addressing the specific challenges posed by ViT models. The proposed framework and strategies provide valuable insights into optimizing communication and computational resources in edge computing environments. Future work in this area could explore the application of these strategies to other types of models and investigate the scalability of the framework for larger-scale deployments.
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