Enabling Machine Learning on the Edge using SRAM Conserving Efficient Neural Networks Execution Approach
暂无分享,去创建一个
[1] John G. Breslin,et al. Edge2Guard: Botnet Attacks Detecting Offline Models for Resource-Constrained IoT Devices , 2021, 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops).
[2] Forrest N. Iandola,et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.
[3] A. Robert Calderbank,et al. DCFNet: Deep Neural Network with Decomposed Convolutional Filters , 2018, ICML.
[4] Muhammad Intizar Ali,et al. Adaptive Strategy to Improve the Quality of Communication for IoT Edge Devices , 2020, 2020 IEEE 6th World Forum on Internet of Things (WF-IoT).
[5] Ran El-Yaniv,et al. Binarized Neural Networks , 2016, ArXiv.
[6] Bo Chen,et al. MnasNet: Platform-Aware Neural Architecture Search for Mobile , 2018, 2019 IEEE/CVF Conference on Computer Vision and Pattern Recognition (CVPR).
[7] Peter Corcoran,et al. Smart Speaker Design and Implementation with Biometric Authentication and Advanced Voice Interaction Capability , 2022, AICS.
[8] Geoffrey E. Hinton,et al. Distilling the Knowledge in a Neural Network , 2015, ArXiv.
[9] Roberto Cipolla,et al. PoseNet: A Convolutional Network for Real-Time 6-DOF Camera Relocalization , 2015, 2015 IEEE International Conference on Computer Vision (ICCV).
[10] John G. Breslin,et al. SRAM optimized porting and execution of machine learning classifiers on MCU-based IoT devices: demo abstract , 2021, ICCPS.
[11] John G. Breslin,et al. TinyML Benchmark: Executing Fully Connected Neural Networks on Commodity Microcontrollers , 2021, 2021 IEEE 7th World Forum on Internet of Things (WF-IoT).
[12] John G. Breslin,et al. Ultra-fast Machine Learning Classifier Execution on IoT Devices without SRAM Consumption , 2021, 2021 IEEE International Conference on Pervasive Computing and Communications Workshops and other Affiliated Events (PerCom Workshops).
[13] Muhammad Intizar Ali,et al. RCE-NN: a five-stage pipeline to execute neural networks (CNNs) on resource-constrained IoT edge devices , 2020, IOT.
[14] Muhammad Intizar Ali,et al. Avoid Touching Your Face: A Hand-to-face 3D Motion Dataset (COVID-away) and Trained Models for Smartwatches , 2020, IOT Companion.
[15] Dumitru Erhan,et al. Going deeper with convolutions , 2014, 2015 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[16] George Papandreou,et al. Rethinking Atrous Convolution for Semantic Image Segmentation , 2017, ArXiv.
[17] Quoc V. Le,et al. EfficientNet: Rethinking Model Scaling for Convolutional Neural Networks , 2019, ICML.
[18] Vijay Vasudevan,et al. Learning Transferable Architectures for Scalable Image Recognition , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[19] Kilian Q. Weinberger,et al. Densely Connected Convolutional Networks , 2016, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[20] Bo Chen,et al. Quantization and Training of Neural Networks for Efficient Integer-Arithmetic-Only Inference , 2017, 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition.
[21] Forrest N. Iandola,et al. SqueezeNet: AlexNet-level accuracy with 50x fewer parameters and <1MB model size , 2016, ArXiv.
[22] Song Han,et al. MCUNet: Tiny Deep Learning on IoT Devices , 2020, NeurIPS.
[23] Shuchang Zhou,et al. EAST: An Efficient and Accurate Scene Text Detector , 2017, 2017 IEEE Conference on Computer Vision and Pattern Recognition (CVPR).
[24] Muhammad Intizar Ali,et al. Edge2Train: a framework to train machine learning models (SVMs) on resource-constrained IoT edge devices , 2020, IOT.
[25] John G. Breslin,et al. OWSNet: Towards Real-time Offensive Words Spotting Network for Consumer IoT Devices , 2021, 2021 IEEE 7th World Forum on Internet of Things (WF-IoT).
[26] J. Breslin,et al. Demo Abstract: Porting and Execution of Anomalies Detection Models on Embedded Systems in IoT , 2021 .