Design possibilities and challenges of DNN models: a review on the perspective of end devices

Hussain, Hanan; Tamizharasan, P. S.; Rahul, C. S.

doi:10.1007/s10462-022-10138-z

Cited by 31 publications

(16 citation statements)

References 121 publications

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“…On the other hand, a comprehensive overview of existing optimization techniques to deploy DNN accelerator on FPGA is provided in [18]. Moreover, [19] reviews prior efforts to deploy DNN models on the end devices efficiently. The design ideas include the types of DNN models, hardware and software requirements for the development, resource constraints imposed by the computing devices, and optimization techniques.…”

Section: A Papers With Similar Backgroundmentioning

confidence: 99%

Dependable DNN Accelerator for Safety-Critical Systems: A Review on the Aging Perspective

Moghaddasi,

Gorgin,

Lee

2023

IEEE Access

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Nowadays, artificial intelligence (AI) and deep learning (DL) progressively adapt to various spheres of our lives. These disciplines contain safety-critical applications such as autonomous driving with a high risk of human injury in the case of malfunction, requiring a high promise of dependability. Even the dependability becomes more crucial as shrinking CMOS technology feature size worsens the resilience concerns due to factors like aging. This paper addresses the overarching dependability issue of advanced deep neural networks (DNN) accelerators from the aging perspective. Especially, a comprehensive survey and taxonomy of techniques used to evaluate and mitigate aging effects are introduced. We cover different aging effects like permanent faults, timing errors, and lifetime issues. We review research by the layer-wise approach and categorize several resilience classes to bring out major features. The concluding part of this review highlights the questions answered and several future research directions. This study is expected to benefit researchers in different areas of DNN deployment, especially the dependability of this emergent paradigm.

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Section: A Papers With Similar Backgroundmentioning

confidence: 99%

Dependable DNN Accelerator for Safety-Critical Systems: A Review on the Aging Perspective

Moghaddasi,

Gorgin,

Lee

2023

IEEE Access

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“…Hussain et al. 119 focused on the usage of different control strategies in chemical processes. Firstly, the authors applied predictive control which is well suited for the optimization of open‐loop nonlinear systems.…”

Section: Application Of Ai‐based Controllers On Nonlinear Processesmentioning

confidence: 99%

“…Due to their ability to preserve information in continuous memory locations, ANNs are well‐suited for real‐time control applications. However, ANNs require substantial computational resources as they rely on trial‐and‐error techniques and need precise, ordered data to function effectively 118, 119.…”

Section: Control Theorymentioning

confidence: 99%

State‐of‐the‐Art Review on the Applications of Nonlinear and Artificial Intelligence‐Based Controllers in Petrochemical Processes

Ansari

Taqvi

2023

ChemBioEng Reviews

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Petrochemical industries are facing severe challenges in controlling nonlinear processes which are to be analyzed and monitored to develop efficient control models for improved efficiency and productivity. This review focuses on the control theory of petrochemical processes along with linear and nonlinear controllers with their limitations as well as on the application of artificial intelligence (AI)‐based controllers in multiple sectors of petrochemical industries. The findings and results from the review prove that the AI control technique is a promising one in the future for uncertain dynamic systems. The paper also covers a review of the vinyl chloride monomer (VCM) process and a recent control study on the plant. Being a nonlinear system, VCM requires advanced control techniques to control the uncertain dynamics of the process. The study shows a gap in the development of control strategies applied to VCM in terms of sustainability, efficiency, and environment‐concerned issues. Valuable insights for researchers, practitioners, and engineers engaged in process control and automation in the petrochemical industry are provided.

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“…It learns from the 1) as [44], Each convolutional layer uses the Equ. ( 1) to extract the features; further, the 1D-max-pooling layer helps the proposed DL-based hybrid model activate the utmost features and reduce the size of the convoluted feature map [45], which is computed using the Equ. ( 2) as,…”

Section: Adaboostmentioning

confidence: 99%

Edge-HetIoT Defense against DDoS attack using LearningTechniques

Mahadik

Pawar

Muthalagu

2022

Preprint

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The heterogeneous nature of the internet-of-thing (IoT) is gaining popularity and, simultaneously, faces rising security issues. The distributed denial of service (DDoS) attack is the most significant security threat addressed in the research. The research proposes edge-heterogeneous IoT (HetIoT) centric defense IDS that aids the HetIoT infrastructure in detecting and blocking victim traffic near the network edge. The Edge-HetIoT defense IDS helps to address significant issues such as performance and security due to proximity to the local network. The research focuses on six learning techniques, including five machine learning (ML) classifiers, namely, ID3, NB, RF, LR, and AdaBoost, and the proposed deep learning (DL)-based hybrid model (i.e., CNN+LSTM). These learning techniques are trained and tested using the real-time benchmark-dataset CICDDoS2019 and consider binary and multiclass (14 classes) classification. The performance is analyzed and evaluated against six classifiers to determine which classification model performs best in detecting and classifying various DDoS attacks. The proposed DL-based hybrid model outperforms when compared against ID3, NB, RF, LR, and AdaBoost. The proposed DL-based hybrid model successfully detects and classifies MSSQL, NetBIOS, TFTP, NTP, Syn, and Portmap attacks with 100% precision, recall, and f1-score. The overall weighted average precision, recall, and f1-score for the proposed DL-based hybrid model are 92%, 89%, and 90%, respectively.

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Design possibilities and challenges of DNN models: a review on the perspective of end devices

Cited by 31 publications

References 121 publications

Dependable DNN Accelerator for Safety-Critical Systems: A Review on the Aging Perspective

Dependable DNN Accelerator for Safety-Critical Systems: A Review on the Aging Perspective

State‐of‐the‐Art Review on the Applications of Nonlinear and Artificial Intelligence‐Based Controllers in Petrochemical Processes

Edge-HetIoT Defense against DDoS attack using LearningTechniques

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