mlsanityData to 7 Oct 2026

Distributed Deep Learning Inference Acceleration using Seamless Collaboration in Edge Computing

arXiv 2207.11294 ↗First posted 22 Jul 2022cs.DC cs.AIcited by 4

On the map: Efficient LLM inference and serving → Early exit and edge inference

Abstract (arXiv)

This paper studies inference acceleration using distributed convolutional neural networks (CNNs) in collaborative edge computing. To ensure inference accuracy in inference task partitioning, we consider the receptive-field when performing segment-based partitioning. To maximize the parallelization between the communication and computing processes, thereby minimizing the total inference time of an inference task, we design a novel task collaboration scheme in which the overlapping zone of the sub-tasks on secondary edge servers (ESs) is executed on the host ES, named as HALP. We further extend HALP to the scenario of multiple tasks. Experimental results show that HALP can accelerate CNN inference in VGG-16 by 1.7-2.0x for a single task and 1.7-1.8x for 4 tasks per batch on GTX 1080TI and JETSON AGX Xavier, which outperforms the state-of-the-art work MoDNN. Moreover, we evaluate the service reliability under time-variant channel, which shows that HALP is an effective solution to ensure high service reliability with strict service deadline.

Key facts. Cited by 4 papers in the map (data through 7 Oct 2026). Placed in Early exit and edge inference, one of 12 directions in Efficient LLM inference and serving. Builds on 2 of its 12 references that are in the map. First posted on arXiv 22 Jul 2022.

Builds on

2 of 12 references.

References · 2

  1. 2014Very Deep Convolutional Networks for Large-Scale Image Recognitioncited by 15,267
  2. 2017Understanding the Effective Receptive Field in Deep Convolutional Neural Networkscited by 549

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Cited by year

Citing papers per year: 2022: 3; 2023: 0; 2024: 0; 2025: 1.

4 papers.

  1. 2022Efficient High-Resolution Deep Learning: A Surveycited by 9
  2. 2022Graph Reinforcement Learning-based CNN Inference Offloading in Dynamic Edge Computingcited by 4
  3. 2022Design and Prototyping Distributed CNN Inference Acceleration in Edge Computingcited by 0
  4. 2025Beyond Imaging: Vision Transformer Digital Twin Surrogates for 3D+T Biological Tissue Dynamicscited by 0

Similar, not cited

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  1. 2022Receptive Field-based Segmentation for Distributed CNN Inference Acceleration in Collaborative Edge Computing
  2. 2025Collaborative Inference Acceleration with Non-Penetrative Tensor Partitioning
  3. 2026CANS: Accelerating Multiuser Collaborative Edge Inference via Cooperative Autodidactic NeuroSurgeon
  4. 2022Fault-Tolerant Collaborative Inference through the Edge-PRUNE Framework
  5. 2025DistrEE: Distributed Early Exit of Deep Neural Network Inference on Edge Devices
  6. 2023DISCO: Distributed Inference with Sparse Communications
  7. 2025Occult: Optimizing Collaborative Communication across Experts for Accelerated Parallel MoE Training and Inference
  8. 2023Adaptive Early Exiting for Collaborative Inference over Noisy Wireless Channels