Distributing the Cloud into Fog and Edge: New Weather in IoT Based Deep Learning

Naveen, Soumyalatha; Kounte, Manjunath R

doi:10.1007/978-981-16-6407-6_64

Cited by 2 publications

(1 citation statement)

References 23 publications

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“…Edge intelligence [1], [2] pushes intelligence from the cloud to edge devices for faster results prediction for many real-time applications such as self-driving, augmented reality, and intelligent surveillance systems. Due to the minimum latency requirement of real-time applications, there is a shift from an artificial intelligent (AI)-based cloud computing approach to edge/fog [3] devices. Hence edge intelligence [4] reduces the response time and network bandwidth required for data movement from the device to the cloud infrastructure for processing and getting back the result.…”

Section: Introductionmentioning

confidence: 99%

Compact optimized deep learning model for edge: a review

Naveen,

Kounte

2023

IJECE

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<p>Most real-time computer vision applications, such as pedestrian detection, augmented reality, and virtual reality, heavily rely on convolutional neural networks (CNN) for real-time decision support. In addition, edge intelligence is becoming necessary for low-latency real-time applications to process the data at the source device. Therefore, processing massive amounts of data impact memory footprint, prediction time, and energy consumption, essential performance metrics in machine learning based internet of things (IoT) edge clusters. However, deploying deeper, dense, and hefty weighted CNN models on resource-constraint embedded systems and limited edge computing resources, such as memory, and battery constraints, poses significant challenges in developing the compact optimized model. Reducing the energy consumption in edge IoT networks is possible by reducing the computation and data transmission between IoT devices and gateway devices. Hence there is a high demand for making energy-efficient deep learning models for deploying on edge devices. Furthermore, recent studies show that smaller compressed models achieve significant performance compared to larger deep-learning models. This review article focuses on state-of-the-art techniques of edge intelligence, and we propose a new research framework for designing a compact optimized deep learning (DL) model deployment on edge devices.</p>

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Section: Introductionmentioning

confidence: 99%

Compact optimized deep learning model for edge: a review

Naveen,

Kounte

2023

IJECE

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Optimized Convolutional Neural Network at the IoT edge for image detection using pruning and quantization

Naveen,

Kounte

2024

Multimed Tools Appl

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Most real-time computer vision applications heavily rely on Convolutional Neural Network (CNN) based models, for image classification and recognition. Due to the computationally and memory-intensive nature of the CNN model, it’s challenging to deploy on resource-constrained Internet of Things (IoT) devices to enable Edge intelligence for real-time decision-making. Edge intelligence requires minimum inference latency, memory footprint, and energy-efficient model. This work aims to develop an energy-efficient deep learning accelerator using a 3-stage pipeline: Training, Weight-pruning, and Quantization to reduce the model size and optimize the resources. First, we employ YOLOv3, a CNN architecture to detect objects in an image on the trained data. In addition, a sparse network of YOLO has been created by using pruning, which helps to improve the network’s performance and efficiency by reducing the computational requirements. Finally, we utilize 8-bit quantization to reduce the precision of the weights and activations, in a neural network. The evaluation of our proposed model shows that combining pruning and 8-bit quantization improves the efficiency and performance of the model. While pruning shows a decline of 80.39% in model parameters. The combination of 8-bit quantization results in an improvement in inference latency by 22.72% compared to existing SQuantization approach and a reduction of energy consumption by 29.41%.

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Distributing the Cloud into Fog and Edge: New Weather in IoT Based Deep Learning

Cited by 2 publications

References 23 publications

Compact optimized deep learning model for edge: a review

Compact optimized deep learning model for edge: a review

Optimized Convolutional Neural Network at the IoT edge for image detection using pruning and quantization

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