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Survey and Enhancements on Deploying LSTM Recurrent neural networks on Embedded Systems

Survey and Enhancements on Deploying LSTM Recurrent Neural N...

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IEEE International conference on Communications (ICC)

作者： Ghalid Abib Florian Castel Nissrine Satouri Hossam Afifi Adel Mounir Said SAMOVAR - Télécom SudParis Institut Polytechnique de Paris Palaiseau France Télécom SudParis Institut Polytechnique de Paris Palaiseau France Switching Department National Telecommunication Institute (NTI) Cairo Egypt

The real implementation of a recurrent neural network (RNN) in a low complexity IoT device is evaluated in order to predict the time series of power consumption in tertiary buildings. The RNN type long short-term memory (LSTM) algorithm is adapted for a 32-bit microcontroller unit (MCU) and the backpropagation (BP) algorithm is implemented in-house. We therefore demonstrate that Intelligent IoT (IIoT) devices, such as the Espressif ESP32 MCU, not only implement neural networks (NNs), but also learn on their own. The resulting IIoT architecture has been proven to operate efficiently and compared to the traditional computer-based learning platform. The selected results confirm that stand-alone IoT devices are a truly efficient solution that adds flexibility to the architecture, reduces storage and computation costs, and is more energy-friendly. As a conclusion, it is practically more efficient to exploit low-power and processing-time IIoT for our prediction use case rather than relying on server based distributed systems.

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An Efficient Hybrid Deep neural network for Intelligence Quotient Detection from Brain Magnetic Resonance Imaging

An Efficient Hybrid Deep Neural Network for Intelligence Quo...

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International conference on Web Research (ICWR)

作者： Shayan Rostami Hossein Sadr Ali Ashraf Mojdeh Nazari Department of Computer Engineering Ayandegan Institute of Education Tonekabon Iran Department of Artificial Intelligence in Medicine Faculty of Advanced Technologies in Medicine Iran University of Medical Sciences Tehran Iran Clinical Research Development Unit of Poursina Hospital Guilan University of Medical Sciences Rasht Iran Department of Health Information Technology and Management School of Allied Medical Sciences Shahid Beheshti University of Medical Sciences Tehran Iran

ISBN: (数字)9798331508913

ISBN: (纸本)9798331508920

Accurate IQ detection through non-invasive methods, such as brain MRI analysis, can play a critical role in the early diagnosis and prevention of neurological disorders. This study proposes a hybrid approach that leverages the feature extraction power of Convolutional neural networks (CNN) and the robust classification capabilities of the XGBoost algorithm to enhance the accuracy of IQ prediction models. CNNs effectively process MRI images by extracting complex visual features, while XGBoost optimizes classification performance through ensemble learning techniques. The integration of these methods results in a more precise and reliable IQ detection system. Furthermore, the model's architecture and data processing techniques offer potential for adaptation in web-based diagnostic platforms and large-scale, distributed medical research applications. This study presents an innovative approach by combining convolutional neural networks (VGG16 and ResNet-50) with the XGBoost algorithm to classify intelligence levels from brain MRI images. Unlike previous studies that used CNNs solely for feature extraction, this research employs XGBoost for final classification, achieving an 83 % accuracy in the VGG16+XGBoost model, showing a notable improvement over the 73 % reported in the most similar prior study. The use of 3D NIfTI data and sagittal slices for enhanced data analysis is also a key innovation of this research.

关键词： Adaptation models Visualization Technological innovation Accuracy Three-dimensional displays Magnetic resonance imaging Feature extraction Brain modeling Classification algorithms Convolutional neural networks

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A Cost-Efficient Failure-Tolerant Scheme for distributed DNN Training

A Cost-Efficient Failure-Tolerant Scheme for Distributed DNN...

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IEEE International conference on Computer Design: VLSI in Computers and Processors, (ICCD)

作者： Menglei Chen Yu Hua Rong Bai Jianming Huang WNLO Huazhong University of Science and Technology Wuhan China

distributed deep neural network (DNN) training is important to support artificial intelligence (AI) applications, such as image classification, natural language processing, and autonomous driving. Unfortunately, the distributed property makes the DNN training vulnerable to system failures. Check-pointing is generally used to support failure tolerance, which however suffers from high runtime overheads. In order to enable high-performance and low-latency checkpointing, we propose a lightweight checkpointing system for distributed DNN training, called LightCheck. To reduce the checkpointing overheads, we leverage fine-grained asynchronous checkpointing by pipelining checkpointing in a layer-wise way. To further decrease the checkpointing latency, we leverage the software-hardware co-design methodology by coalescing new hardware devices into our checkpointing system via a persistent memory (PM) manager. Experimental results on six representative real-world DNN models demonstrate that LightCheck offers more than 10× higher check-pointing frequency with lower runtime overheads than state-of-the-art checkpointing schemes. We have released the open-source codes for public use in https://***/LighT-chenml/***.

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Real-Time FPGA Implementation of CNN-Based distributed Fiber Optic Vibration Event Recognition Method

Real-Time FPGA Implementation of CNN-Based Distributed Fiber...

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Asia Communications and Photonics conference and Exhibition (ACP)

作者： Zhongyao Luo Zhao Ge Hao Wu Ming Tang National Laboratory for Optoelectronics (WNLO) & National Engineering Laboratory for Next Generation Internet Access System School of Optical and Electronic Information Huazhong University of Science and Technology Wuhan China

utilizing optical fibers to detect and pinpoint vibrations, distributed Optical Fiber Vibration Sensing (DVS) technology provides real-time monitoring and surveillance of wide-reaching areas. This field has been leveraging Convolutional neural networks (CNN). Recently, a study has accomplished end-to-end vibration event recognition, enabling utilization of CNN-based DVS algorithms as real-time embedded system for edge computing in practical application situations. Considering the power consumption of central processing unit (CPU) and graphics processing unit (GPU), and the inflexibility of application-specific integrated circuit (ASIC), field-Programmable gate array (FPGA) is the optimal computing platform for the system. This paper proposes to compress pre-trained network and adopt a novel hardware structure, to design a fully on-chip, pipelined inference accelerator for CNN-based DVS algorithm, without fine tuning or re-training. This design allows for real-time processing with low power consumption and system requirement. An examination has been executed on an existing DVS algorithm based on a 40-layer CNN model comprising 2.7 million parameters. It is completely implemented on-chip, pipelined, with no reduction in accuracy.

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Characterizing ML Training Performance at the Tactical Edge 4

Characterizing ML Training Performance at the Tactical Edge

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conference on Artificial Intelligence and Machine Learning for Multi-Domain Operations Applications IV

作者： Alshabanah, Abdulla Balasubramanian, Keshav Krishnamachari, Bhaskar Annavaram, Murali Univ Southern Calif Los Angeles CA 90007 USA

ISBN: (纸本)9781510651036;9781510651029

The commercial industry has been working to develop ever-larger and more capable machine learning (ML) models (such as recent models from OpenAI, Microsoft and Google with more than ten billion parameters) for everything from general language processing to computer vision that have larger and larger computational, memory and other resource requirements. These powerful models generally need hardware accelerators to accommodate their workloads. GPU systems have been a popular choice among users and deep learning model designers either for their ability to run the inherently parallel deep learning workloads efficiently or for their large memory resource that would fit large deep learning models. We profile and analyze different deep learning workloads using various GPUs and configurations, emphasizing how deep learning architectures have diverse compute requirements. We analyze three popular deep learning workloads: Ultralytics's You-Only-Look-Once model (Yolov5) on COCO dataset, Bidirectional Encoder Representations from Transformers (BERT), and Deep Learning Recommendation Model (DLRM) on the Criteo Kaggle Display Advertising Challenge Dataset. This work aims to shed light on the performance bottlenecks when using GPU systems as accelerators for training recent deep learning models.

关键词： GPU deep learning convolutional neural networks distributed computing computer architecture

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Prediction Algorithm of Physical Education Achievement Based on BP neural network 2021

Prediction Algorithm of Physical Education Achievement Based...

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2021 International conference on Aviation Safety and Information Technology

作者： Jinglian Chi Bohai University China

ISBN: (纸本)9781450390422

Achievement prediction is one of the important contents of the research on future development strategy of competitive sports. In this study, take the men's 800 meters final results in 2008 as an example, use BP neural network algorithm to explore the method of achievement prediction on competitive sports. Firstly, expounds the basic principle of BP neural network algorithm, and establish the BP neural network structure diagram; Then, introduces the training process and establish process of BP neural network model, and construct the training process flow chart; Finally, begin the prediction for the achievements of men's 800m based on BP neural network, and through error analysis to test the accuracy of the prediction. BP neural network has the characteristics of massively parallel, distributed processing, self-organizing, self-learning, also has the ability of nonlinear mapping, adaptive ability, generalization ability and fault tolerant ability.

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An Effective Recurrent neural network (RNN) based Intrusion Detection via Bi-directional Long Short-Term Memory

An Effective Recurrent Neural Network (RNN) based Intrusion ...

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2021 International conference on Intelligent Technologies, CONIT 2021

作者： Sivamohan, S. Sridhar, S.S. Krishnaveni, S. Dept of Computer Science and Engineering SRMIST Kattankulathur Chennai India Dept of Software Engineering SRMIST Kattankulathur Chennai India

ISBN: (纸本)9781728185835

The evolution of communication and information systems has raised the volume of data distributed through the internet. As an effect, a majority of digital resources have been increased, so does the challenge of cybersecurity. Intrusion detection systems (IDSs) are closely connected to a holistic approach for preventing cyberattacks. Due to the high utilization of network traffic in the cyber world, conventional machine learning approaches used in intrusion detection systems are becomes ineffective. Recently evolved deep learning techniques are successfully applied in the detection and classification of threats at both the network and host levels, with a focus on deep learning. This study proposed an efficient IDS based on Recurrent neural network (RNN) via Bi-directional Long Short- Term Memory (RNN BiLSTM). The strategy uses a two-step mechanism to develop the expertise of the suggested solution to address network problems. This research aims to determine the algorithm's processing time and increase attack classification accuracy. The proposed model was evaluated on the CICIDS2017 intrusion detection dataset. The Random forest and Principal component analysis algorithms were used to detecting the valuable features and eliminating the unwanted features from the given dataset. The findings revealed that BLSTM outperform all other RNN architectures in terms of classification accura 98.48%. © 2021 IEEE.

关键词： Long short-term memory

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A GPU Architecture Aware Fine-Grain Pruning Technique for Deep neural networks 27th

A GPU Architecture Aware Fine-Grain Pruning Technique for De...

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27th International European conference on Parallel and distributed Computing (Euro-Par)

作者： Choi, Kyusik Yang, Hoeseok Ajou Univ Dept ECE Suwon 16499 Gyeonggi South Korea

ISBN: (纸本)9783030856656;9783030856649

The model size and computation requirement of Deep Convolutional neural networks (DNNs) have ever increased as their applications to various real-life use-cases, e.g., autonomous driving, are getting more pervasive and popular. While DNN workloads are executed on Graphics processing Units (GPUs) in many cases, it is not trivial to improve the inference speed through the conventional DNN weight pruning techniques, due to the parallel architecture of GPUs. On the other hand, the coarse-grain pruning, also known as structured sparsity or structured pruning, can speedup the inference, but cause significant losses of accuracy. In this paper, we propose two fine-grain DNN pruning techniques that are aware of the underlying GPU architecture. For that, we analyze the hierarchical architecture of parallel processing elements and memory of GPU to identify the finest possible pruning where the removed weights can be safely skipped during the inference. The effectiveness of the proposed techniques has been evaluated with VGG16. Compared to the existing pruning techniques, the proposed methods result in significantly improved inference speed with less accuracy drop.

关键词： DNN Architecture-aware pruning GPU

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33rd International conference on Scientific and Statistical Database Management, SSDBM 2021, Proceedings

33rd International Conference on Scientific and Statistical ...

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33rd International conference on Scientific and Statistical Database Management, SSDBM 2021

ISBN: (纸本)9781450384131

The proceedings contain 32 papers. The topics discussed include: unsupervised anomaly detection for time series with outlier exposure;accelerating depth-first traversal by graph ordering;subarray skyline query processing in array databases;caching support for range query processing on bitmap indices;Bio-SODA: enabling natural language question answering over knowledge graphs without training data;distributed enumeration of four node graphlets at quadrillion-scale;NF-GNN: network flow graph neural networks for malware detection and classification;online landmark-based batch processing of shortest path queries;and missing data patterns: from theory to an application in the steel industry.

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Adaptive distributed Convolutional neural network Inference at the network Edge with ADCNN 20

Adaptive Distributed Convolutional Neural Network Inference ...

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49th International conference on Parallel processing (ICPP)

作者： Zhang, Sai Qian Lin, Jieyu Zhang, Qi Harvard Univ Cambridge MA 02138 USA Univ Toronto Toronto ON Canada Microsoft Redmond WA USA

ISBN: (纸本)9781450388160

The emergence of the Internet of Things (IoT) has led to a remarkable increase in the volume of data generated at the network edge. In order to support real-time smart IoT applications, massive amounts of data generated from edge devices need to be processed using methods such as deep neural networks (DNNs) with low latency. To improve application performance and minimize resource cost, enterprises have begun to adopt Edge computing, a computation paradigm that advocates processing input data locally at the network edge. However, as edge nodes are often resource-constrained, running data-intensive DNN inference tasks on each individual edge node often incurs high latency, which seriously limits the practicality and effectiveness of this model. In this paper, we study the problem of distributed execution of inference tasks on edge clusters for Convolutional neural networks (CNNs), one of the most prominent models of DNN. Unlike previous work, we present Fully Decomposable Spatial Partition (FDSP), which naturally supports resource heterogeneity and dynamicity in edge computing environments. We then present a compression technique that further reduces network communication overhead. Our system, called ADCNN, provides up to 2.8x speed up compared to state-of-the-art approaches, while achieving a competitive inference accuracy.

关键词： Edge computing Convolutional neural networks distributed inference

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