EdgeSight: Enabling Modeless and Cost-Efficient Inference at the Edge

• URL: http://arxiv.org/abs/2405.19213v2
• Date: Wed, 15 Jan 2025 04:17:38 GMT
• Title: EdgeSight: Enabling Modeless and Cost-Efficient Inference at the Edge
• Authors: ChonLam Lao, Jiaqi Gao, Ganesh Ananthanarayanan, Aditya Akella, Minlan Yu,
• Abstract summary: We propose EdgeSight, a system that provides cost-efficient modeless inference at the edge. Our experimental results show that EdgeSight outperforms existing systems by up to 1.6x in P99 latency for modeless services. Our FPGA prototype demonstrates similar performance at certain accuracy levels, with a power consumption reduction of up to 3.34x.
• Score: 10.110832890670997
• License:
• Abstract: Traditional ML inference is evolving toward modeless inference, which abstracts the complexity of model selection from users, allowing the system to automatically choose the most appropriate model for each request based on accuracy and resource requirements. While prior studies have focused on modeless inference within data centers, this paper tackles the pressing need for cost-efficient modeless inference at the edge -- particularly within its unique constraints of limited device memory, volatile network conditions, and restricted power consumption. To overcome these challenges, we propose EdgeSight, a system that provides cost-efficient EdgeSight serving for diverse DNNs at the edge. EdgeSight employs an edge-data center (edge-DC) architecture, utilizing confidence scaling to reduce the number of model options while meeting diverse accuracy requirements. Additionally, it supports lossy inference in volatile network environments. Our experimental results show that EdgeSight outperforms existing systems by up to 1.6x in P99 latency for modeless services. Furthermore, our FPGA prototype demonstrates similar performance at certain accuracy levels, with a power consumption reduction of up to 3.34x.

Related papers

• Task-Oriented Real-time Visual Inference for IoVT Systems: A Co-design Framework of Neural Networks and Edge Deployment [61.20689382879937]
  Task-oriented edge computing addresses this by shifting data analysis to the edge. Existing methods struggle to balance high model performance with low resource consumption. We propose a novel co-design framework to optimize neural network architecture.
  arXiv  Detail & Related papers  (2024-10-29T19:02:54Z)
• Efficient Multi-Object Tracking on Edge Devices via Reconstruction-Based Channel Pruning [0.2302001830524133]
  We propose a neural network pruning method specifically tailored to compress complex networks, such as those used in modern MOT systems. We achieve model size reductions of up to 70% while maintaining a high level of accuracy and further improving performance on the Jetson Orin Nano.
  arXiv  Detail & Related papers  (2024-10-11T12:37:42Z)
• Heterogeneity-Aware Resource Allocation and Topology Design for Hierarchical Federated Edge Learning [9.900317349372383]
  Federated Learning (FL) provides a privacy-preserving framework for training machine learning models on mobile edge devices. Traditional FL algorithms, e.g., FedAvg, impose a heavy communication workload on these devices. We propose a two-tier HFEL system, where edge devices are connected to edge servers and edge servers are interconnected through peer-to-peer (P2P) edge backhauls. Our goal is to enhance the training efficiency of the HFEL system through strategic resource allocation and topology design.
  arXiv  Detail & Related papers  (2024-09-29T01:48:04Z)
• Towards Robust and Efficient Cloud-Edge Elastic Model Adaptation via Selective Entropy Distillation [56.79064699832383]
  We establish a Cloud-Edge Elastic Model Adaptation (CEMA) paradigm in which the edge models only need to perform forward propagation. In our CEMA, to reduce the communication burden, we devise two criteria to exclude unnecessary samples from uploading to the cloud.
  arXiv  Detail & Related papers  (2024-02-27T08:47:19Z)
• DVFO: Learning-Based DVFS for Energy-Efficient Edge-Cloud Collaborative Inference [12.095934624748686]
  We propose DVFO, a novel DVFS-enabled edge-cloud collaborative inference framework. It automatically co-optimizes the CPU, GPU and memory frequencies of edge devices, and the feature maps to be offloaded to cloud servers. It significantly reduces the energy consumption by 33% on average, compared to state-of-the-art schemes.
  arXiv  Detail & Related papers  (2023-06-02T07:00:42Z)
• Energy-efficient Task Adaptation for NLP Edge Inference Leveraging Heterogeneous Memory Architectures [68.91874045918112]
  adapter-ALBERT is an efficient model optimization for maximal data reuse across different tasks. We demonstrate the advantage of mapping the model to a heterogeneous on-chip memory architecture by performing simulations on a validated NLP edge accelerator.
  arXiv  Detail & Related papers  (2023-03-25T14:40:59Z)
• DeepFT: Fault-Tolerant Edge Computing using a Self-Supervised Deep Surrogate Model [12.335763358698564]
  We propose DeepFT to proactively avoid system overloads and their adverse effects. DeepFT uses a deep surrogate model to accurately predict and diagnose faults in the system. It offers a highly scalable solution as the model size scales by only 3 and 1 percent per unit increase in the number of active tasks and hosts.
  arXiv  Detail & Related papers  (2022-12-02T16:51:58Z)
• Computational Intelligence and Deep Learning for Next-Generation Edge-Enabled Industrial IoT [51.68933585002123]
  We investigate how to deploy computational intelligence and deep learning (DL) in edge-enabled industrial IoT networks. In this paper, we propose a novel multi-exit-based federated edge learning (ME-FEEL) framework. In particular, the proposed ME-FEEL can achieve an accuracy gain up to 32.7% in the industrial IoT networks with the severely limited resources.
  arXiv  Detail & Related papers  (2021-10-28T08:14:57Z)
• Adaptive Dynamic Pruning for Non-IID Federated Learning [3.8666113275834335]
  Federated Learning(FL) has emerged as a new paradigm of training machine learning models without sacrificing data security and privacy. We present an adaptive pruning scheme for edge devices in an FL system, which applies dataset-aware dynamic pruning for inference acceleration on Non-IID datasets.
  arXiv  Detail & Related papers  (2021-06-13T05:27:43Z)
• Learning to Solve the AC-OPF using Sensitivity-Informed Deep Neural Networks [52.32646357164739]
  We propose a deep neural network (DNN) to solve the solutions of the optimal power flow (ACOPF) The proposed SIDNN is compatible with a broad range of OPF schemes. It can be seamlessly integrated in other learning-to-OPF schemes.
  arXiv  Detail & Related papers  (2021-03-27T00:45:23Z)
• A Privacy-Preserving-Oriented DNN Pruning and Mobile Acceleration Framework [56.57225686288006]
  Weight pruning of deep neural networks (DNNs) has been proposed to satisfy the limited storage and computing capability of mobile edge devices. Previous pruning methods mainly focus on reducing the model size and/or improving performance without considering the privacy of user data. We propose a privacy-preserving-oriented pruning and mobile acceleration framework that does not require the private training dataset.
  arXiv  Detail & Related papers  (2020-03-13T23:52:03Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.