A Technologically Agnostic Framework for Cyber-Physical and IoT Processing-in-Memory-based Systems Simulation

Santos, Paulo C.; Lima, João Paulo Cardoso de; Moura, Rafael Fão de; Ahmed, Hameeza; Alves, Marco A. Z.; Beck, Antonio Carlos Schneider; Carro, Luigi

doi:10.1016/j.micpro.2019.06.002

Cited by 12 publications

(6 citation statements)

References 15 publications

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“…In traditional CPU-like accelerators (e.g., General Purpose Processor (GPP), GPU, Tensorflow), although the performance is commonly measured from the processing logic's point of view, the LLC is the default data entry point, making it the main bottleneck in terms of on-chip bandwidth [113,114]. For instance, using a full GPP or GPU as a memory logic elevates the complexity of the design and does not intrinsically solve the main problems faced with NDP integration, namely programming model, virtual address translation, and cache coherence, as these challenges are objectives in many studies [115,116,117]. Moreover, the costs in terms of power and area are severe, being an actual limiting factor [112].…”

Section: A Full-stack Processormentioning

confidence: 99%

“…In the 2010's, mainly due to the advent of 3D-stacking integration, this approach reappeared as a better fit for power and area constrained devices. However, this type of NDP requires innovative solutions for programming models, cache coherence, and virtual memory support [115,116,117]. Many works [7,15,16,32,52,67,68,69,72,74,79,80], proposed the use of custom FU-like logic to exploit the bandwidth of memories.…”

Section: B Simple Functional Unitsmentioning

confidence: 99%

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Survey on Near-Data Processing: Applications and Architectures

Santos

Moreira

Cordeiro

et al. 2021

JICS

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One of the main challenges for modern processors is the data transfer between processor and memory. Such data movement implies high latency and high energy consumption. In this context, Near-Data Processing (NDP) proposals have started to gain acceptance as an accelerator device. Such proposals alleviate the memory bottleneck by moving instructions to data whereabouts. The first proposals date back to the 1990s, but it was only in the 2010s that we could observe an increase in papers addressing NDP. It occurred together with the appearance of 3D-stacked chips with logic and memory stacked layers. This survey presents a brief history of these accelerators, focusing on the applications domains migrated to near-data and the proposed architectures. We also introduce a new taxonomy to classify such architectural proposals according to their data distance.

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Section: A Full-stack Processormentioning

confidence: 99%

Section: B Simple Functional Unitsmentioning

confidence: 99%

Survey on Near-Data Processing: Applications and Architectures

Santos

Moreira

Cordeiro

et al. 2021

JICS

Self Cite

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“…Cyber Physical System (CPS) has evolved as an integral support for improving the service quality of autonomous and distributed communication environments. Diverse computing, communication, and control systems are unified under CPS that is coupled to the IoT paradigm [11,12]. Integration of such control system aids multi-faced benefits for IoT by aiding dynamic and reconfigurable service support and reliable resource oriented functions.…”

mentioning

confidence: 99%

A Response-Aware Traffic Offloading Scheme Using Regression Machine Learning for User-Centric Large-Scale Internet of Things

Manogaran

Srivastava

Muthu³

et al. 2021

IEEE Internet Things J.

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Resource allocation and management in an Internet of Things (IoT) paradigm requires precise request and response processing irrespective of its scalability support. Unpredictable traffic pattern and user density demands reliable offloading for handling user request traffic and service response. Considering the need for large-scale IoT in account of its interoperability and heterogeneous support, this manuscript introduces responseaware traffic offloading scheme (RTOS) for delay-sensitive user requests. This offloading scheme is supported by multivariate spline regression machine learning model for classifying traffic for reducing the failure rate. The splines are adaptive based on the classified traffic for performing independent and shared offloading. The computation process for determining the offloading model is inherited from the cyber physical system (CPS) coupled with the IoT-Cloud architecture. The information from the knowledge base and event logs are exploited for decisionmaking in employing the offloading method for the classified traffic. The simulation analysis of this scheme shows that it is effective in improving the request processing ratio and reducing processing, response time, and delay. The simulation is performed for the varying user density and traffic flows.

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“…The system can accurately and effectively identify abnormal images of tourist attractions. In [4], the author proposes an efficient IoT PIM system that can calculate real image recognition applications. The proposed architecture is able to process 6 times more frames per second than the baseline, while increasing energy efficiency by 30 times.…”

mentioning

confidence: 99%

“…Image recognition flowchart of the Internet of Things image recognition system Influence of Different Parameters of Convolutional Neural Network on the Image Recognition Rate of the Internet of Things(1) The impact of transformation times on the experimental results In the CUB-200-2011 and CIFAR-100 databases, the recognition rates corresponding to different transformation times are used(Figure 4), where k (k ∈[1,4]) represent…”

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confidence: 99%

Deep Learning Based Image Recognition for 5G Smart IoT Applications

Ning

2020

Preprint

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With the advent of the 5G era，the development of massive data learning algorithms and in-depth research on neural networks, deep learning methods are widely used in image recognition tasks. However, there is currently a lack of methods for identifying and classifying efficiently Internet of Things (IoT) images. This paper develops an IoT image recognition system based on deep learning, i.e., uses convolutional neural networks (CNN) to construct image recognition algorithms, and uses principal component analysis (PCA) and linear discriminant analysis (LDA) to extract image features, respectively. The effectiveness of the two PCA and LDA image recognition methods is verified through experiments. And when the image feature dimension is 25, the best image recognition effect can be obtained. The main classifier used for image recognition in the IoT is the support vector machine (SVM), and the SVM and CNN are trained by using the database of this paper. At the same time, the effectiveness of the two for image recognition is checked, and then the trained classifier is used for image recognition. It is found that a CNN and SVM-based secondary classification IoT image recognition method improves the accuracy of image recognition. The secondary classification method combines the characteristics of the SVM and CNN image recognition methods, and the accuracy of the image recognition method is verified to provide an effective improvement through experimental verification.

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A Technologically Agnostic Framework for Cyber-Physical and IoT Processing-in-Memory-based Systems Simulation

Cited by 12 publications

References 15 publications

Survey on Near-Data Processing: Applications and Architectures

Survey on Near-Data Processing: Applications and Architectures

A Response-Aware Traffic Offloading Scheme Using Regression Machine Learning for User-Centric Large-Scale Internet of Things

Deep Learning Based Image Recognition for 5G Smart IoT Applications

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