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BranchyNet: Fast Inference via Early Exiting from Deep Neural Networks
[article]
2017
arXiv
pre-print
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The architecture allows prediction results for a large portion of test samples to exit the network early via these branches when samples can already be inferred with high confidence. ...
However, the improved performance of additional layers in a deep network comes at the cost of added latency and energy usage in feedforward inference. ...
BranchyNet is a toolbox for researchers to use on any deep network models for fast inference. ...
arXiv:1709.01686v1
fatcat:fq6z7z3xdbfvhpwhj7zdjmvooa
E^2CM: Early Exit via Class Means for Efficient Supervised and Unsupervised Learning
[article]
2022
arXiv
pre-print
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cost and network accuracy. ...
This makes it particularly useful for neural network training in low-power devices, as in wireless edge networks. ...
For BranchyNet, we add 2 branches to the original network as in [9] . ...
arXiv:2103.01148v3
fatcat:2nb5wvsg2fhbjn6tn6vqqbslui
Adaptive Deep Neural Network Inference Optimization with EENet
[article]
2023
arXiv
pre-print
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Instead of having every sample go through all DNN layers during prediction, EENet learns an early exit scheduler, which can intelligently terminate the inference earlier for certain predictions, which ...
inference budget. ...
We optimize the weights using Adam optimizer with the learning rate of 3e-5 on validation data and set α cost = 10. ...
arXiv:2301.07099v2
fatcat:ihxebof4bzgfxkezfoophnr42i
An open source framework based on Kafka-ML for DDNN inference over the Cloud-to-Things continuum
2021
Journal of systems architecture
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These processing units, located between 30 2 J o u r n a l P r e -p r o o f Journal Pre-proof 45 2. effective communication layers to interconnect, discover, and communicate 3 J o u r n a l P r e -p r ...
The current dependency of Artificial Intelligence (AI) systems on Cloud computing implies higher transmission latency and bandwidth consumption. ...
-215 ("Advanced Monitoring System Based on Deep Learning Services in Fog"). ...
doi:10.1016/j.sysarc.2021.102214
fatcat:hkm5vtksijawhnfgzp6f7dtd3m
ApproxNet: Content and Contention-Aware Video Analytics System for Embedded Clients
[article]
2021
arXiv
pre-print
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None of the current approximation techniques for object classification DNNs can adapt to changing runtime conditions, e.g., changes in resource availability on the device, the content characteristics, ...
Videos take a lot of time to transport over the network, hence running analytics on the live video on embedded or mobile devices has become an important system driver. ...
We cannot compare to BranchyNet as it is not designed and evaluated for video analytics and thus not suitable for our datasets. BranchyNet paper evaluates it on small image dataset: MNIST and CIFAR. ...
arXiv:1909.02068v5
fatcat:5pa6tzarrfbvvlrmliufylj7ae
Early Exiting-Enabled Siamese Tracking for Edge Intelligence Applications
2021
IEEE Access
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As shown in Fig. 1 , the exit branches are located after every convolutional layer except for the first layer. ...
Inspired by BranchyNet [21] , SiamEE introduces three additional exit branches to the network. ...
doi:10.1109/access.2021.3119604
fatcat:ddyenpr6a5dtzl7px5vni4zexa
Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks
2018
2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition
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The effectiveness of SDPoint, as both a cost-adjustable inference approach and a regularizer, is validated through extensive experiments on image classification. ...
We propose a novel approach for cost-adjustable inference in CNNs -Stochastic Downsampling Point (SDPoint). ...
For cost-adjustable inference, we evaluate every branch, and make all samples "exit" at the same branch. ...
doi:10.1109/cvpr.2018.00827
dblp:conf/cvpr/KuenK00YST18
fatcat:fmjdxcxt2fgjpn55kt3or5oeau
Network Public Opinion Prediction and Control Based on Edge Computing and Artificial Intelligence New Paradigm
2021
Wireless Communications and Mobile Computing
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In this paper, an adaptive edge service placement mechanism based on online learning and a predictive edge service migration method based on factor graph model are proposed to solve the edge computing ...
First, the time series of the development of online chaotic public opinion is a platform for vectorized collection of keyword index trends using the theory of chaotic phase space reconstruction. ...
In the collaborative inference stage, the edge server and the mobile terminal carry out collaborative inference on the deep learning model according to the optimal network branch and segmentation point ...
doi:10.1155/2021/5566647
fatcat:n7taocrdpzafjgzz6hkkbl5eoy
Efficient Post-Training Augmentation for Adaptive Inference in Heterogeneous and Distributed IoT Environments
[article]
2024
arXiv
pre-print
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Our proposed approach enables the creation of EENN optimized for IoT environments and can reduce the inference cost of Deep Learning applications on embedded and fog platforms, while also significantly ...
reducing the search cost - making it more accessible for scientists and engineers in industry and research. ...
The responsibility for the content of this publication lies with the author. ...
arXiv:2403.07957v1
fatcat:5ghmygr3pjgxdd46gjnspbgane
Distributed and Collaborative High Speed Inference Deep Learning for Mobile Edge with Topological Dependencies
2020
IEEE Transactions on Cloud Computing
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The proposed approach exploits topological dependencies of the edge using a resource-optimized graph neural network (GNN) version with an accelerated inference. ...
To cope, this work proposes compressed collaborative learning based on momentum correction called cCGNN-edge with better scalability while preserving accuracy. ...
Branchynet-enabled training with multiple branches to accelerate the inference. ...
doi:10.1109/tcc.2020.2978846
fatcat:aw2lsygdpzg77fwdtivdhjwopq
Using Early Exits for Fast Inference in Automatic Modulation Classification
[article]
2023
arXiv
pre-print
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2023 pre-print arXiv:2308.11100v1 fatcat:3kq2bayhpvgpxnljoq6dvcmgcm
We present and analyze four early exiting architectures and a customized multi-branch training algorithm for this problem. ...
This paper proposes the application of early exiting (EE) techniques for DL models used for AMC to accelerate inference. ...
Early Exit Architectures -Effect of Exit Location We first thoroughly study the effect of the location of the early exit (i.e., the architecture choice) on the classification accuracy, the frequency of ...
arXiv:2308.11100v2
fatcat:ltpkzcrvovfjhkxhouocy5prxm
Stochastic Downsampling for Cost-Adjustable Inference and Improved Regularization in Convolutional Networks
[article]
2018
arXiv
pre-print
Preserved Fulltext
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The effectiveness of SDPoint, as both a cost-adjustable inference approach and a regularizer, is validated through extensive experiments on image classification. ...
We propose a novel approach for cost-adjustable inference in CNNs - Stochastic Downsampling Point (SDPoint). ...
For cost-adjustable inference, we evaluate every branch, and make all samples "exit" at the same branch. ...
arXiv:1801.09335v1
fatcat:o7fvsxtdlbfz3ot5mdrsolf7wu
MSD: Multi-Self-Distillation Learning via Multi-classifiers within Deep Neural Networks
[article]
2019
arXiv
pre-print
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This reduces the gap of capacity between different classifiers, and improves the effectiveness of applying MSD. ...
However, as the huge computational overhead, these networks could not be applied on mobile devices or other low latency scenes. ...
With the ever-increasing demand for improved performance, the development of deeper networks has greatly increased the latency and computational cost of inference. ...
arXiv:1911.09418v3
fatcat:yngwpzadcza2dgywpt7ofmq6pm
Edge Intelligence: Paving the Last Mile of Artificial Intelligence With Edge Computing
2019
Proceedings of the IEEE
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We then provide an overview of the overarching architectures, frameworks, and emerging key technologies for deep learning model toward training/inference at the network edge. ...
To this end, we conduct a comprehensive survey of the recent research efforts on EI. Specifically, we first review the background and motivation for AI running at the network edge. ...
With BranchyNet, the standard DNN model structure is modified by adding exit branches at certain layer locations. ...
doi:10.1109/jproc.2019.2918951
fatcat:d53vxmklgfazbmzjhsq3tuoama
Temporal Decisions: Leveraging Temporal Correlation for Efficient Decisions in Early Exit Neural Networks
[article]
2024
arXiv
pre-print
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However, deploying models on embedded devices poses a challenge due to their resource limitations. This can impact the model's inference accuracy and latency. ...
We evaluate their effectiveness in health monitoring, image classification, and wake-word detection tasks. ...
The responsibility for the content of this publication lies with the author. ...
arXiv:2403.07958v1
fatcat:rsm5wf2kdzfprj3ofywnou7sly
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