I-SplitEE: Image classification in Split Computing DNNs with Early Exits

• URL: http://arxiv.org/abs/2401.10541v1
• Date: Fri, 19 Jan 2024 07:44:32 GMT
• Title: I-SplitEE: Image classification in Split Computing DNNs with Early Exits
• Authors: Divya Jyoti Bajpai, Aastha Jaiswal, Manjesh Kumar Hanawal
• Abstract summary: Large size of Deep Neural Networks (DNNs) hinders deploying them on resource-constrained devices like edge, mobile, and IoT platforms. Our work presents an innovative unified approach merging early exits and split computing. I-SplitEE is an online unsupervised algorithm ideal for scenarios lacking ground truths and with sequential data.
• Score: 5.402030962296633
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: The recent advances in Deep Neural Networks (DNNs) stem from their exceptional performance across various domains. However, their inherent large size hinders deploying these networks on resource-constrained devices like edge, mobile, and IoT platforms. Strategies have emerged, from partial cloud computation offloading (split computing) to integrating early exits within DNN layers. Our work presents an innovative unified approach merging early exits and split computing. We determine the 'splitting layer', the optimal depth in the DNN for edge device computations, and whether to infer on edge device or be offloaded to the cloud for inference considering accuracy, computational efficiency, and communication costs. Also, Image classification faces diverse environmental distortions, influenced by factors like time of day, lighting, and weather. To adapt to these distortions, we introduce I-SplitEE, an online unsupervised algorithm ideal for scenarios lacking ground truths and with sequential data. Experimental validation using Caltech-256 and Cifar-10 datasets subjected to varied distortions showcases I-SplitEE's ability to reduce costs by a minimum of 55% with marginal performance degradation of at most 5%.

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)
• A Converting Autoencoder Toward Low-latency and Energy-efficient DNN Inference at the Edge [4.11949030493552]
  We present CBNet, a low-latency and energy-efficient deep neural network (DNN) inference framework tailored for edge devices. It utilizes a "converting" autoencoder to efficiently transform hard images into easy ones. CBNet achieves up to 4.8x speedup in inference latency and 79% reduction in energy usage compared to competing techniques.
  arXiv  Detail & Related papers  (2024-03-11T08:13:42Z)
• SplitEE: Early Exit in Deep Neural Networks with Split Computing [0.0]
  Deep Neural Networks (DNNs) have drawn attention because of their outstanding performance on various tasks. deploying full-fledged DNNs in resource-constrained devices is difficult due to their large size. We propose combining two approaches by using early exits in split computing.
  arXiv  Detail & Related papers  (2023-09-17T07:48:22Z)
• Attention-based Feature Compression for CNN Inference Offloading in Edge Computing [93.67044879636093]
  This paper studies the computational offloading of CNN inference in device-edge co-inference systems. We propose a novel autoencoder-based CNN architecture (AECNN) for effective feature extraction at end-device. Experiments show that AECNN can compress the intermediate data by more than 256x with only about 4% accuracy loss.
  arXiv  Detail & Related papers  (2022-11-24T18:10:01Z)
• Dynamic Split Computing for Efficient Deep Edge Intelligence [78.4233915447056]
  We introduce dynamic split computing, where the optimal split location is dynamically selected based on the state of the communication channel. We show that dynamic split computing achieves faster inference in edge computing environments where the data rate and server load vary over time.
  arXiv  Detail & Related papers  (2022-05-23T12:35:18Z)
• SplitPlace: AI Augmented Splitting and Placement of Large-Scale Neural Networks in Mobile Edge Environments [13.864161788250856]
  This work proposes an AI-driven online policy, SplitPlace, that uses Multi-Armed-Bandits to intelligently decide between layer and semantic splitting strategies. SplitPlace places such neural network split fragments on mobile edge devices using decision-aware reinforcement learning. Our experiments show that SplitPlace can significantly improve the state-of-the-art in terms of average response time, deadline violation rate, inference accuracy, and total reward by up to 46, 69, 3 and 12 percent respectively.
  arXiv  Detail & Related papers  (2022-05-21T16:24:47Z)
• An Adaptive Device-Edge Co-Inference Framework Based on Soft Actor-Critic [72.35307086274912]
  High-dimension parameter model and large-scale mathematical calculation restrict execution efficiency, especially for Internet of Things (IoT) devices. We propose a new Deep Reinforcement Learning (DRL)-Soft Actor Critic for discrete (SAC-d), which generates the emphexit point, emphexit point, and emphcompressing bits by soft policy iterations. Based on the latency and accuracy aware reward design, such an computation can well adapt to the complex environment like dynamic wireless channel and arbitrary processing, and is capable of supporting the 5G URL
  arXiv  Detail & Related papers  (2022-01-09T09:31:50Z)
• EffCNet: An Efficient CondenseNet for Image Classification on NXP BlueBox [0.0]
  Edge devices offer limited processing power due to their inexpensive hardware, and limited cooling and computational resources. We propose a novel deep convolutional neural network architecture called EffCNet for edge devices.
  arXiv  Detail & Related papers  (2021-11-28T21:32:31Z)
• Early-exit deep neural networks for distorted images: providing an efficient edge offloading [69.43216268165402]
  Edge offloading for deep neural networks (DNNs) can be adaptive to the input's complexity. We introduce expert side branches trained on a particular distortion type to improve against image distortion. This approach increases the estimated accuracy on the edge, improving the offloading decisions.
  arXiv  Detail & Related papers  (2021-08-20T19:52:55Z)
• Online Limited Memory Neural-Linear Bandits with Likelihood Matching [53.18698496031658]
  We study neural-linear bandits for solving problems where both exploration and representation learning play an important role. We propose a likelihood matching algorithm that is resilient to catastrophic forgetting and is completely online.
  arXiv  Detail & Related papers  (2021-02-07T14:19:07Z)

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.