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Advancements in Artificial neural networks and Tensorflow's Role in Democratizing ML

Advancements in Artificial Neural Networks and Tensorflow's ...

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International conference on Wavelet Active Media Technology and Information processing (ICWAMTIP)

作者： Wajiha Abdul Shakir California State University Northridge California the United States

ISBN: (数字)9798331519254

ISBN: (纸本)9798331519261

This paper provides a comprehensive overview of artificial neural networks (ANNs), exploring their theoretical foundations, practical applications, and recent advancements. I delve into the basic constructs of ANNs, discuss vital algorithms, and examine deep neural networks (DNNs) with real-world applications. Highlighting the strengths and weaknesses of various neural network variants, I emphasize the significant impact of Google's TensorFlow in simplifying machine learning processes. Released in 2015, TensorFlow's architecture and programming model has revolutionized the field by enabling scalable, distributed, and efficient machine learning development. Furthermore, I explore the potential future of machine learning, including cutting-edge research in Artificial General Intelligence (AGI) and the pursuit of universal learning algorithms.

关键词： Machine learning algorithms Artificial general intelligence Machine learning Learning (artificial intelligence) Information processing Programming Media Internet Convolutional neural networks Logistics

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CNN Framework for Automatic Segmentation of Breast Section from Thermal Images

CNN Framework for Automatic Segmentation of Breast Section f...

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2023 International conference on System, Computation, Automation and networking, ICSCAN 2023

作者： Rama, A. Sudeepa, K.B. Arunmozhi, S. Mohammed, Mazin Abed Ali, Aqeel Rajinikanth, V. Saveetha School of Engineering Simats Department of CSE TN Chennai602105 India Nmam Institute of Technology Department of Computer Science & Engineering Nitte Karnataka Udupi574110 India Manakula Vinayagar Institute of Technology Department of Electronics and Communication Engineering TN Puducherry605 107 India College of Computer Science and Information Technology University of Anbar Department of Artificial Intelligence Anbar31001 Iraq Al-farahidi University Medical Instruments Engineering Techniques Baghdad10021 Iraq Saveetha School of Engineering Simats Division of Research and Innovation Department of CSE TN Chennai602105 India

ISBN: (纸本)9798350315127

Breast cancer is considered a severe illness in the female society, and if left untreated, it can be fatal. It is always desirable to detect the BC early utilizing a selected imaging strategy. Thermogram supported breast abnormality detection is one of the recent technique and this gives the necessary information in the form of the distributed thermal pattern. This research aims to implement the Convolutional-neural-network (CNN) based segmentation technique to extract breast region from the chosen thermogram. This scheme's multiple stages include: (i) data collecting and processing, (ii) implementation of CNN segmentation to extract the breast, (iii) comparing it to the binary-mask and computing performance metrics, and (iv) performance evaluation and verification of the chosen CNN techniques. Pre-trained CNN segmentations are used in this work to extract the necessary section from the thermogram, and the experimental results show that the VGG-UNet methodology helps to extract the essential region with an enhanced accuracy of 97.260.64% when compared to other CNN approaches. © 2023 IEEE.

关键词： Convolutional neural networks

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network MEMORY FOOTPRINT COMPRESSION THROUGH JOINTLY LEARNABLE CODEBOOKS AND MAPPINGS 12

NETWORK MEMORY FOOTPRINT COMPRESSION THROUGH JOINTLY LEARNAB...

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12th International conference on Learning Representations, ICLR 2024

作者： Yvinec, Edouard Dapogny, Arnaud Bailly, Kevin Sorbonne Université CNRS ISIR 4 Place Jussieu ParisF-75005 France Datakalab 114 boulevard Malesherbes Paris75017 France

The massive interest in deep neural networks (DNNs) for both computer vision and natural language processing has been sparked by the growth in computational power. However, this led to an increase in the memory footprint, to a point where it can be challenging to simply load a model on commodity devices such as mobile phones. To address this limitation, quantization is a favored solution as it maps high precision tensors to a low precision, memory efficient format. In terms of memory footprint reduction, its most effective variants are based on codebooks. These methods, however, suffer from two limitations. First, they either define a single codebook for each tensor, or use a memory-expensive mapping to multiple codebooks. Second, gradient descent optimization of the mapping favors jumps toward extreme values, hence not defining a proximal search. In this work, we propose to address these two limitations. First, we initially group similarly distributed neurons and leverage the re-ordered structure to either apply different scale factors to the different groups, or map weights that fall in these groups to several codebooks, without any mapping overhead. Second, stemming from this initialization, we propose a joint learning of the codebook and weight mappings that bears similarities with recent gradient-based post-training quantization techniques. Third, drawing estimation from straight-through estimation techniques, we introduce a novel gradient update definition to enable a proximal search of the codebooks and their mappings. The proposed jointly learnable codebooks and mappings (JLCM) method allows a very efficient approximation of any DNN: as such, a Llama 7B can be compressed down to 2Go and loaded on 5-year-old smartphones. © 2024 12th International conference on Learning Representations, ICLR 2024. All rights reserved.

关键词： Mapping

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A dataflow architecture with distributed control for DNN acceleration

A dataflow architecture with distributed control for DNN acc...

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Mediterranean conference on Embedded Computing (MECO)

作者： Hana Krichene Rohit Prasad CEA List Université Paris-Saclay Palaiseau France

ISBN: (数字)9798350387568

ISBN: (纸本)9798350387575

The increasing demand for edge computing requires effective Deep neural network (DNN) accelerators that are suitable for resource-limited environments. This paper presents a new method that uses distributed control methodology for DNN acceleration on edge devices. Our architecture offers significant improvements over a similar architecture without such feature, including a remarkable reduction in memory requirements by up to 7×, along with notable speedups of up to 7.42× in DNN processing. Additionally, our design reaches energy efficiency of a maximum of 4300 MOPS/W, demonstrating its potential to address resource constraints while improving DNN performance on edge platforms.

关键词： Performance evaluation Embedded computing Memory management Decentralized control distributed databases Artificial neural networks Energy efficiency

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Feature Selection Using COA with Modified Feedforward neural network for Prediction of Attacks in Cyber-Security

Feature Selection Using COA with Modified Feedforward Neural...

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distributed Computing and Optimization Techniques (ICDCOT), International conference on

作者： Rohith Vallabhaneni Nagamani H S Harshitha P Sumanth S University of the Cumberlands USA Department of Computer Science Govt. Smt VHD Central Institute of Home Science Bengaluru India Department of AIML Nitte Meenakshi Institute of Technology Bengaluru India Department of Computer Science and Applictaions Government College for Women Kolar India

Research on network intrusion detection, prediction, and mitigation systems has been ongoing due to the exponential rise in cyber-attacks in recent times. The prediction of future network invasions is still an open research subject, despite the abundance of intrusion detection systems. Current solutions need feature selection and engineering since they use statistical and/or shallow machine learning techniques to do the job. This study begins with pre-processing the BotNet dataset with feature encoding and scaling. The next step is to use the Coot Optimisation Algorithm (COA) to pick the most significant characteristics. The next step is for the FeedForward neural network (FFNN) model to do the classification. ETLBO is used to determine the model's weight appropriately. Using the adaptive weight approach with the Kent chaotic map improves the ideal performance of the standard TLBO. The projected technique aims to circumvent the primary problems with the original TLBO by not using the local optimum and by maintaining an equilibrium between the search methods. When compared to current methods, the experimental analysis reveals that the suggested model attained a precision of 96.76% and an accuracy of 97.56%.

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A Study on Different Types of Convolutions in Deep Learning in the Area of Lane Detection 1

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International conference on distributed Computing and Optimization Tech-niques (ICDCOT)

作者： Rajalakshmi, T. S. Senthilnathan, R. SRM Inst Sci & Technol Dept Mech Engn Chennai Tamil Nadu India

ISBN: (数字)9789811922817

ISBN: (纸本)9789811922817;9789811922800

One of the key technologies in autonomous vehicles is image based lane detection algorithm. High performance is detected in modern deep learning methods. But in case of challenging areas like congested roads or poor lighting conditions, it is difficult to accurately detect lanes. Global context information is required which can be extracted from limited visual-cue. Moreover, for automotive driver assisting system, like lane keep, collision avoid etc., it is important to know the position of the vehicle i.e., in which lane it is. Due to large varieties in shape and colour of the lane marking, it becomes difficult to solve this task. For this purpose, an initial step on the input image is the image processing, where the data is processed as per the requirement in pixel level semantic segmentation. Then comes in the creation of the semantic segmentation model which is able to process the data. This model can be of different variant based on the computation ability, as well as the parameter handling capacity.

关键词： Convolutional neural network Deep learning techniques Evaluation metric Encoder-decoder Fully convoluted network

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An Anomaly Detection Model for RISC-V in Automotive Applications: A Domain-Specific Accelerator Perspective

An Anomaly Detection Model for RISC-V in Automotive Applicat...

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Euromicro conference on Parallel, distributed and network-Based processing

作者： Elio Vinciguerra Enrico Russo Maurizio Palesi Giuseppe Ascia Department of Electrical Electronic and Computer Engineering University of Catania Catania Italy

ISBN: (数字)9798331524937

ISBN: (纸本)9798331524944

Early anomaly detection in automotive systems is crucial for enhancing user safety and enabling timely corrective actions, thereby minimizing the risks associated with system malfunctions. This paper presents an approach for implementing Artificial Intelligence (AI)-based algorithms for anomaly detection in the automotive domain, leveraging the RISC-V architecture in conjunction with Domain-Specific Accelerators (DSAs). By exploiting the efficiency of DSAs, the proposed system aims to achieve faster anomaly detection compared to traditional processing methods. A detailed comparison is conducted between the performance of executing the AI-based anomaly detection algorithm on the RISC-V core versus offloading it to an optimized hardware accelerator tailored to the specific AI model. The goal of this work is to provide valuable insights into the potential of RISCV and DSAs to enhance AI-driven safety mechanisms, contributing to the development of more reliable automotive systems.

关键词： Program processors neural networks Real-time systems Safety Reliability Artificial intelligence Anomaly detection Hardware acceleration Optimization Monitoring

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distributed Principal Component Analysis with Limited Communication 35

Distributed Principal Component Analysis with Limited Commun...

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35th Annual conference on neural Information processing Systems (NeurIPS)

作者： Alimisis, Foivos Davies, Peter Vandereycken, Bart Alistarh, Dan Univ Geneva Dept Math Geneva Switzerland Univ Surrey Dept Comp Sci Surrey England IST Austria Vienna Austria Neural Magic Inc Somerville MA 02144 USA

ISBN: (纸本)9781713845393

We study efficient distributed algorithms for the fundamental problem of principal component analysis and leading eigenvector computation on the sphere, when the data are randomly distributed among a set of computational nodes. We propose a new quantized variant of Riemannian gradient descent to solve this problem, and prove that the algorithm converges with high probability under a set of necessary spherical-convexity properties. We give bounds on the number of bits transmitted by the algorithm under common initialization schemes, and investigate the dependency on the problem dimension in each case.

关键词： Principal component analysis

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CD-Sched: An Automated Scheduling Framework for Accelerating neural network Training on Shared Memory CPU-DSP Platforms 23

CD-Sched: An Automated Scheduling Framework for Accelerating...

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Proceedings of the 2023 International conference on Power, Communication, Computing and networking Technologies

作者： Yuanyuan Xiao Zhiquan Lai Dongsheng Li National Key Laboratory for Parallel and Distributed Processing National University of Defense Technology China National Key Laboratory of Parallel and Distributed Processing National University of Defense Technology China

ISBN: (纸本)9781450399951

DSP holds significant potential for important applications in Deep neural networks. However, there is currently a lack of research focused on shared-memory CPU-DSP heterogeneous chips. This paper proposes CD-Sched, an automated scheduling framework that aims to address this gap. By predicting the latency of operators on both CPU and DSP, CD-Sched automatically schedules the computation of operators to the appropriate computing device. This scheduling optimization accelerates the computation of individual operators and ultimately improves the overall training time of neural networks. In end-to-end training tasks, CD-Sched can significantly reduce the overall training time, with an average reduction of approximately 10.77%.

关键词： computation offloading

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Fograph: Enabling Real-Time Deep Graph Inference with Fog Computing 22

Fograph: Enabling Real-Time Deep Graph Inference with Fog Co...

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31st ACM Web conference (WWW)

作者： Zeng, Liekang Huang, Peng Luo, Ke Zhang, Xiaoxi Zhou, Zhi Chen, Xu Sun Yat Sen Univ Sch Comp Sci & Engn Guangzhou Peoples R China

ISBN: (纸本)9781450390965

Graph neural networks (GNNs) have gained growing interest in miscellaneous applications owing to their outstanding ability in extracting latent representation on graph structures. To render GNN-based service for IoT-driven smart applications, the traditional model serving paradigm resorts to the cloud by fully uploading the geo-distributed input data to the remote datacenter. However, our empirical measurements reveal the significant communication overhead of such cloud-based serving and highlight the profound potential in applying the emerging fog computing. To maximize the architectural benefits brought by fog computing, in this paper, we present Fograph, a novel distributed real-time GNN inference framework that leverages diverse resources of multiple fog nodes in proximity to IoT data sources. By introducing heterogeneity-aware execution planning and GNN-specific compression techniques, Fograph tailors its design to well accommodate the unique characteristics of GNN serving in fog environment. Prototype-based evaluation and case study demonstrate that Fograph significantly outperforms the state-of-the-art cloud serving and vanilla fog deployment by up to 5.39x execution speedup and 6.84x throughput improvement.

关键词： Fog computing graph neural networks distributed processing model serving

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