Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey

• URL: http://arxiv.org/abs/2208.10498v1
• Date: Mon, 22 Aug 2022 12:12:43 GMT
• Title: Design Automation for Fast, Lightweight, and Effective Deep Learning Models: A Survey
• Authors: Dalin Zhang, Kaixuan Chen, Yan Zhao, Bin Yang, Lina Yao, Christian S. Jensen
• Abstract summary: This survey covers studies of design automation techniques for deep learning models targeting edge computing. It offers an overview and comparison of key metrics that are used commonly to quantify the proficiency of models in terms of effectiveness, lightness, and computational costs. The survey proceeds to cover three categories of the state-of-the-art of deep model design automation techniques.
• Score: 53.258091735278875
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Deep learning technologies have demonstrated remarkable effectiveness in a wide range of tasks, and deep learning holds the potential to advance a multitude of applications, including in edge computing, where deep models are deployed on edge devices to enable instant data processing and response. A key challenge is that while the application of deep models often incurs substantial memory and computational costs, edge devices typically offer only very limited storage and computational capabilities that may vary substantially across devices. These characteristics make it difficult to build deep learning solutions that unleash the potential of edge devices while complying with their constraints. A promising approach to addressing this challenge is to automate the design of effective deep learning models that are lightweight, require only a little storage, and incur only low computational overheads. This survey offers comprehensive coverage of studies of design automation techniques for deep learning models targeting edge computing. It offers an overview and comparison of key metrics that are used commonly to quantify the proficiency of models in terms of effectiveness, lightness, and computational costs. The survey then proceeds to cover three categories of the state-of-the-art of deep model design automation techniques: automated neural architecture search, automated model compression, and joint automated design and compression. Finally, the survey covers open issues and directions for future research.

Related papers

• Computation-efficient Deep Learning for Computer Vision: A Survey [121.84121397440337]
  Deep learning models have reached or even exceeded human-level performance in a range of visual perception tasks. Deep learning models usually demand significant computational resources, leading to impractical power consumption, latency, or carbon emissions in real-world scenarios. New research focus is computationally efficient deep learning, which strives to achieve satisfactory performance while minimizing the computational cost during inference.
  arXiv  Detail & Related papers  (2023-08-27T03:55:28Z)
• FreeREA: Training-Free Evolution-based Architecture Search [17.202375422110553]
  FreeREA is a custom cell-based evolution NAS algorithm that exploits an optimised combination of training-free metrics to rank architectures. Our experiments, carried out on the common benchmarks NAS-Bench-101 and NATS-Bench, demonstrate that i) FreeREA is a fast, efficient, and effective search method for models automatic design.
  arXiv  Detail & Related papers  (2022-06-17T11:16:28Z)
• Enable Deep Learning on Mobile Devices: Methods, Systems, and Applications [46.97774949613859]
  Deep neural networks (DNNs) have achieved unprecedented success in the field of artificial intelligence (AI) However, their superior performance comes at the considerable cost of computational complexity. This paper provides an overview of efficient deep learning methods, systems and applications.
  arXiv  Detail & Related papers  (2022-04-25T16:52:48Z)
• SOLIS -- The MLOps journey from data acquisition to actionable insights [62.997667081978825]
  In this paper we present a unified deployment pipeline and freedom-to-operate approach that supports all requirements while using basic cross-platform tensor framework and script language engines. This approach however does not supply the needed procedures and pipelines for the actual deployment of machine learning capabilities in real production grade systems.
  arXiv  Detail & Related papers  (2021-12-22T14:45:37Z)
• Cost-effective Machine Learning Inference Offload for Edge Computing [0.3149883354098941]
  This paper proposes a novel offloading mechanism by leveraging installed-base on-premises (edge) computational resources. The proposed mechanism allows the edge devices to offload heavy and compute-intensive workloads to edge nodes instead of using remote cloud.
  arXiv  Detail & Related papers  (2020-12-07T21:11:02Z)
• A Survey on Large-scale Machine Learning [67.6997613600942]
  Machine learning can provide deep insights into data, allowing machines to make high-quality predictions. Most sophisticated machine learning approaches suffer from huge time costs when operating on large-scale data. Large-scale Machine Learning aims to learn patterns from big data with comparable performance efficiently.
  arXiv  Detail & Related papers  (2020-08-10T06:07:52Z)
• Knowledge Distillation: A Survey [87.51063304509067]
  Deep neural networks have been successful in both industry and academia, especially for computer vision tasks. It is a challenge to deploy these cumbersome deep models on devices with limited resources. Knowledge distillation effectively learns a small student model from a large teacher model.
  arXiv  Detail & Related papers  (2020-06-09T21:47:17Z)

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.