E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized Learning

• URL: http://arxiv.org/abs/2409.08369v1
• Date: Thu, 12 Sep 2024 19:30:22 GMT
• Title: E-QUARTIC: Energy Efficient Edge Ensemble of Convolutional Neural Networks for Resource-Optimized Learning
• Authors: Le Zhang, Onat Gungor, Flavio Ponzina, Tajana Rosing,
• Abstract summary: Ensembling models like Convolutional Neural Networks (CNNs) result in high memory and computing overhead, preventing their deployment in embedded systems. We propose E-QUARTIC, a novel Energy Efficient Edge Ensembling framework to build ensembles of CNNs targeting Artificial Intelligence (AI)-based embedded systems.
• Score: 9.957458251671486
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Ensemble learning is a meta-learning approach that combines the predictions of multiple learners, demonstrating improved accuracy and robustness. Nevertheless, ensembling models like Convolutional Neural Networks (CNNs) result in high memory and computing overhead, preventing their deployment in embedded systems. These devices are usually equipped with small batteries that provide power supply and might include energy-harvesting modules that extract energy from the environment. In this work, we propose E-QUARTIC, a novel Energy Efficient Edge Ensembling framework to build ensembles of CNNs targeting Artificial Intelligence (AI)-based embedded systems. Our design outperforms single-instance CNN baselines and state-of-the-art edge AI solutions, improving accuracy and adapting to varying energy conditions while maintaining similar memory requirements. Then, we leverage the multi-CNN structure of the designed ensemble to implement an energy-aware model selection policy in energy-harvesting AI systems. We show that our solution outperforms the state-of-the-art by reducing system failure rate by up to 40% while ensuring higher average output qualities. Ultimately, we show that the proposed design enables concurrent on-device training and high-quality inference execution at the edge, limiting the performance and energy overheads to less than 0.04%.

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)
• Revisiting DNN Training for Intermittently Powered Energy Harvesting Micro Computers [0.6721767679705013]
  This study introduces and evaluates a novel training methodology tailored for Deep Neural Networks in energy-constrained environments. We propose a dynamic dropout technique that adapts to both the architecture of the device and the variability in energy availability. Preliminary results demonstrate that this strategy provides 6 to 22 percent accuracy improvements compared to the state of the art with less than 5 percent additional compute.
  arXiv  Detail & Related papers  (2024-08-25T01:13:00Z)
• Federated Learning With Energy Harvesting Devices: An MDP Framework [5.852486435612777]
  Federated learning (FL) requires edge devices to perform local training and exchange information with a parameter server. A critical challenge in practical FL systems is the rapid energy depletion of battery-limited edge devices. We apply energy harvesting technique in FL systems to extract ambient energy for continuously powering edge devices.
  arXiv  Detail & Related papers  (2024-05-17T03:41:40Z)
• Machine Learning Insides OptVerse AI Solver: Design Principles and Applications [74.67495900436728]
  We present a comprehensive study on the integration of machine learning (ML) techniques into Huawei Cloud's OptVerse AI solver. We showcase our methods for generating complex SAT and MILP instances utilizing generative models that mirror multifaceted structures of real-world problem. We detail the incorporation of state-of-the-art parameter tuning algorithms which markedly elevate solver performance.
  arXiv  Detail & Related papers  (2024-01-11T15:02:15Z)
• Energy-frugal and Interpretable AI Hardware Design using Learning Automata [5.514795777097036]
  A new machine learning algorithm, called the Tsetlin machine, has been proposed. In this paper, we investigate methods of energy-frugal artificial intelligence hardware design. We show that frugal resource allocation can provide decisive energy reduction while also achieving robust and interpretable learning.
  arXiv  Detail & Related papers  (2023-05-19T15:11:18Z)
• Evaluating Distribution System Reliability with Hyperstructures Graph Convolutional Nets [74.51865676466056]
  We show how graph convolutional networks and hyperstructures representation learning framework can be employed for accurate, reliable, and computationally efficient distribution grid planning. Our numerical experiments show that the proposed Hyper-GCNNs approach yields substantial gains in computational efficiency.
  arXiv  Detail & Related papers  (2022-11-14T01:29:09Z)
• Fluid Batching: Exit-Aware Preemptive Serving of Early-Exit Neural Networks on Edge NPUs [74.83613252825754]
  "smart ecosystems" are being formed where sensing happens concurrently rather than standalone. This is shifting the on-device inference paradigm towards deploying neural processing units (NPUs) at the edge. We propose a novel early-exit scheduling that allows preemption at run time to account for the dynamicity introduced by the arrival and exiting processes.
  arXiv  Detail & Related papers  (2022-09-27T15:04:01Z)
• EAFL: Towards Energy-Aware Federated Learning on Battery-Powered Edge Devices [3.448338949969246]
  Federated learning (FL) is a newly emerged branch of AI that facilitates edge devices to collaboratively train a global machine learning model without centralizing data and with privacy by default. In large-scale deployments, client heterogeneity is the norm which impacts training quality such as accuracy, fairness, and time. We develop EAFL, an energy-aware FL selection method that considers energy consumption to maximize the participation of heterogeneous target devices.
  arXiv  Detail & Related papers  (2022-08-09T02:15:45Z)
• EVE: Environmental Adaptive Neural Network Models for Low-power Energy Harvesting System [8.16411986220709]
  Energy harvesting technology that harvests energy from ambient environment is a promising alternative to batteries for powering those devices. This paper proposes EVE, an automated machine learning framework to search for desired multi-models with shared weights for energy harvesting IoT devices. Experimental results show that the neural networks models generated by EVE is on average 2.5X faster than the baseline models without pruning and shared weights.
  arXiv  Detail & Related papers  (2022-07-14T20:53:46Z)
• Energy-Efficient Multi-Orchestrator Mobile Edge Learning [54.28419430315478]
  Mobile Edge Learning (MEL) is a collaborative learning paradigm that features distributed training of Machine Learning (ML) models over edge devices. In MEL, possible coexistence of multiple learning tasks with different datasets may arise. We propose lightweight algorithms that can achieve near-optimal performance and facilitate the trade-offs between energy consumption, accuracy, and solution complexity.
  arXiv  Detail & Related papers  (2021-09-02T07:37:10Z)
• Reconfigurable Intelligent Surface Assisted Mobile Edge Computing with Heterogeneous Learning Tasks [53.1636151439562]
  Mobile edge computing (MEC) provides a natural platform for AI applications. We present an infrastructure to perform machine learning tasks at an MEC with the assistance of a reconfigurable intelligent surface (RIS) Specifically, we minimize the learning error of all participating users by jointly optimizing transmit power of mobile users, beamforming vectors of the base station, and the phase-shift matrix of the RIS.
  arXiv  Detail & Related papers  (2020-12-25T07:08:50Z)

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.