Enabling Incremental Knowledge Transfer for Object Detection at the Edge

• URL: http://arxiv.org/abs/2004.05746v2
• Date: Sun, 7 Jun 2020 13:31:22 GMT
• Title: Enabling Incremental Knowledge Transfer for Object Detection at the Edge
• Authors: Mohammad Farhadi Bajestani, Mehdi Ghasemi, Sarma Vrudhula and Yezhou Yang
• Abstract summary: Object detection using deep neural networks (DNNs) involves a huge amount of computation. shallow neural network (SHNN) is deployed on user-end device to detect objects in observing environment. SHNN knowledge can be obtained from a powerful edge device connected to the user-end device through LAN or Wi-Fi.
• Score: 25.22732861751805
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: Object detection using deep neural networks (DNNs) involves a huge amount of computation which impedes its implementation on resource/energy-limited user-end devices. The reason for the success of DNNs is due to having knowledge over all different domains of observed environments. However, we need a limited knowledge of the observed environment at inference time which can be learned using a shallow neural network (SHNN). In this paper, a system-level design is proposed to improve the energy consumption of object detection on the user-end device. An SHNN is deployed on the user-end device to detect objects in the observing environment. Also, a knowledge transfer mechanism is implemented to update the SHNN model using the DNN knowledge when there is a change in the object domain. DNN knowledge can be obtained from a powerful edge device connected to the user-end device through LAN or Wi-Fi. Experiments demonstrate that the energy consumption of the user-end device and the inference time can be improved by 78% and 71% compared with running the deep model on the user-end device.

Related papers

• Accelerating Object Detection with YOLOv4 for Real-Time Applications [0.276240219662896]
  Convolutional Neural Network (CNN) have emerged as a powerful tool for recognizing image content and in computer vision approach for most problems. This paper introduces the brief introduction of deep learning and object detection framework like Convolutional Neural Network(CNN)
  arXiv  Detail & Related papers  (2024-10-17T17:44:57Z)
• Spiking CenterNet: A Distillation-boosted Spiking Neural Network for Object Detection [15.043707655842592]
  Spiking Neural Networks (SNNs) are a promising approach to address this challenge. We propose Spiking CenterNet for object detection on event data. Our work is the first approach that takes advantage of knowledge distillation in the field of spiking object detection.
  arXiv  Detail & Related papers  (2024-02-02T10:23:03Z)
• 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)
• Quantum Transfer Learning for Wi-Fi Sensing [19.06876644658677]
  We investigate transfer learning to mitigate domain shift in human monitoring tasks when Wi-Fi settings and environments change over time. As a proof-of-concept study, we consider quantum neural networks (QNN) as well as classical deep neural networks (DNN) for the future quantum-ready society.
  arXiv  Detail & Related papers  (2022-05-17T19:16:21Z)
• Object Detection with Spiking Neural Networks on Automotive Event Data [0.0]
  We propose to train spiking neural networks (SNNs) directly on data coming from event cameras to design fast and efficient automotive embedded applications. In this paper, we conducted experiments on two automotive event datasets, establishing new state-of-the-art classification results for spiking neural networks.
  arXiv  Detail & Related papers  (2022-05-09T14:39:47Z)
• Hybrid SNN-ANN: Energy-Efficient Classification and Object Detection for Event-Based Vision [64.71260357476602]
  Event-based vision sensors encode local pixel-wise brightness changes in streams of events rather than image frames. Recent progress in object recognition from event-based sensors has come from conversions of deep neural networks. We propose a hybrid architecture for end-to-end training of deep neural networks for event-based pattern recognition and object detection.
  arXiv  Detail & Related papers  (2021-12-06T23:45:58Z)
• CondenseNeXt: An Ultra-Efficient Deep Neural Network for Embedded Systems [0.0]
  A Convolutional Neural Network (CNN) is a class of Deep Neural Network (DNN) widely used in the analysis of visual images captured by an image sensor. In this paper, we propose a neoteric variant of deep convolutional neural network architecture to ameliorate the performance of existing CNN architectures for real-time inference on embedded systems.
  arXiv  Detail & Related papers  (2021-12-01T18:20:52Z)
• Achieving Real-Time LiDAR 3D Object Detection on a Mobile Device [53.323878851563414]
  We propose a compiler-aware unified framework incorporating network enhancement and pruning search with the reinforcement learning techniques. Specifically, a generator Recurrent Neural Network (RNN) is employed to provide the unified scheme for both network enhancement and pruning search automatically. The proposed framework achieves real-time 3D object detection on mobile devices with competitive detection performance.
  arXiv  Detail & Related papers  (2020-12-26T19:41:15Z)
• Energy Drain of the Object Detection Processing Pipeline for Mobile Devices: Analysis and Implications [77.00418462388525]
  This paper presents the first detailed experimental study of a mobile augmented reality (AR) client's energy consumption and the detection latency of executing Convolutional Neural Networks (CNN) based object detection. Our detailed measurements refine the energy analysis of mobile AR clients and reveal several interesting perspectives regarding the energy consumption of executing CNN-based object detection.
  arXiv  Detail & Related papers  (2020-11-26T00:32:07Z)
• Boosting Deep Neural Networks with Geometrical Prior Knowledge: A Survey [77.99182201815763]
  Deep Neural Networks (DNNs) achieve state-of-the-art results in many different problem settings. DNNs are often treated as black box systems, which complicates their evaluation and validation. One promising field, inspired by the success of convolutional neural networks (CNNs) in computer vision tasks, is to incorporate knowledge about symmetric geometrical transformations.
  arXiv  Detail & Related papers  (2020-06-30T14:56:05Z)
• Temporal Pulses Driven Spiking Neural Network for Fast Object Recognition in Autonomous Driving [65.36115045035903]
  We propose an approach to address the object recognition problem directly with raw temporal pulses utilizing the spiking neural network (SNN) Being evaluated on various datasets, our proposed method has shown comparable performance as the state-of-the-art methods, while achieving remarkable time efficiency.
  arXiv  Detail & Related papers  (2020-01-24T22:58:55Z)

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.