Pub Date : 2023-02-20DOI: 10.1109/ICAIIC57133.2023.10067021
Kirill Vanin, H. Ryu, A. Safin, O. Kravchenko
When there is any particular interference or jamming signal in the OFDM based wireless communication system, it will be serious problem for the communication even though OFDM (orthogonal frequency division multiplexing) system is inherently robust to interference than the single carrier communication system. Therefore, it is very important to design the counter-measure to this kind of interference. This paper describes OFDM based system with error correction coding in presence of strong spectral concentrated interference (SCI). Using weight function in combination with nonlinear compensation method allows to increase robust against SCI. Several window functions are proposed and compared. Computer simulation shows the signal spectrum improvements.
{"title":"Interference-Robust OFDM Communication System using Weight Functions","authors":"Kirill Vanin, H. Ryu, A. Safin, O. Kravchenko","doi":"10.1109/ICAIIC57133.2023.10067021","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067021","url":null,"abstract":"When there is any particular interference or jamming signal in the OFDM based wireless communication system, it will be serious problem for the communication even though OFDM (orthogonal frequency division multiplexing) system is inherently robust to interference than the single carrier communication system. Therefore, it is very important to design the counter-measure to this kind of interference. This paper describes OFDM based system with error correction coding in presence of strong spectral concentrated interference (SCI). Using weight function in combination with nonlinear compensation method allows to increase robust against SCI. Several window functions are proposed and compared. Computer simulation shows the signal spectrum improvements.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"134078605","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-20DOI: 10.1109/ICAIIC57133.2023.10067076
Roufaida Laidi, L. Khelladi, Meriem Kessaissia, Lyna Ouandjli
Poor sitting posture can lead to a variety of serious diseases raging from spinal disorders to psychological stress. This paper aims to design a sitting posture monitoring system that detects improper postures and notifies the user in real time through a mobile application. The system leverages the use of low-cost EMG sensors, and relies on energy-efficient communication via Bluetooth Low energy (BLE). To ensure bad posture detection, different machine learning algorithms are tested and compared, namely support vector machine (SVM), K-nearest neighbours (KNN), decision tree (DT), random forest (RF), and multi-layer perception (MLP). We formulated the problem as a binary classification (good vs. bad posture) and multi-class classification (good, tilted to the front, right and left). The results of the training performed on a real dataset showed that KNN have the best accuracy (91% accuracy) and execution time (0.0066 ms).
{"title":"Bad Sitting Posture Detection and Alerting System using EMG Sensors and Machine Learning","authors":"Roufaida Laidi, L. Khelladi, Meriem Kessaissia, Lyna Ouandjli","doi":"10.1109/ICAIIC57133.2023.10067076","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067076","url":null,"abstract":"Poor sitting posture can lead to a variety of serious diseases raging from spinal disorders to psychological stress. This paper aims to design a sitting posture monitoring system that detects improper postures and notifies the user in real time through a mobile application. The system leverages the use of low-cost EMG sensors, and relies on energy-efficient communication via Bluetooth Low energy (BLE). To ensure bad posture detection, different machine learning algorithms are tested and compared, namely support vector machine (SVM), K-nearest neighbours (KNN), decision tree (DT), random forest (RF), and multi-layer perception (MLP). We formulated the problem as a binary classification (good vs. bad posture) and multi-class classification (good, tilted to the front, right and left). The results of the training performed on a real dataset showed that KNN have the best accuracy (91% accuracy) and execution time (0.0066 ms).","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"133528754","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-20DOI: 10.1109/ICAIIC57133.2023.10067096
Thi Diem Tran, Huu-Hanh Hoang
Spiking Neural Network (SNN), developing on neuromorphic hardware, is a promising energy-efficient AI paradigm. However, processing over several timesteps reduces the energy benefits of SNNs due to high latency, the number of operations, and memory access costs from acquiring membrane potentials. Furthermore, their non-derivative nature makes SNNs difficult to train properly. To overcome these issues and leverage the full potential of SNNs, in this research, we offer a novel way for training deep SNNs utilizing Batch Normalization Through Time and Iterative Initialization and Retraining techniques. First, the BNTT improves low-latency and low-energy training in SNNs by allowing neurons to handle the spike rate over many timesteps. Second, we can obtain SNNs with up to unit latency pass during inference when applying the Iterative Initialization and Retraining technique during training SNNs. On the CIFAR-10, CIFAR-100, and ImageNet, we achieve cutting-edge SNN performance using a deep neural network with just one timestep. We achieve top-1 accuracy of 91.01%, 71.88%, and 69.8% on CIFAR-10, CIFAR-100, and ImageNet, respectively, using the VGG 16 architecture.
{"title":"Training SNNs Low Latency Utilizing Batch Normalization Through Time and Iterative Initialization Retraining","authors":"Thi Diem Tran, Huu-Hanh Hoang","doi":"10.1109/ICAIIC57133.2023.10067096","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067096","url":null,"abstract":"Spiking Neural Network (SNN), developing on neuromorphic hardware, is a promising energy-efficient AI paradigm. However, processing over several timesteps reduces the energy benefits of SNNs due to high latency, the number of operations, and memory access costs from acquiring membrane potentials. Furthermore, their non-derivative nature makes SNNs difficult to train properly. To overcome these issues and leverage the full potential of SNNs, in this research, we offer a novel way for training deep SNNs utilizing Batch Normalization Through Time and Iterative Initialization and Retraining techniques. First, the BNTT improves low-latency and low-energy training in SNNs by allowing neurons to handle the spike rate over many timesteps. Second, we can obtain SNNs with up to unit latency pass during inference when applying the Iterative Initialization and Retraining technique during training SNNs. On the CIFAR-10, CIFAR-100, and ImageNet, we achieve cutting-edge SNN performance using a deep neural network with just one timestep. We achieve top-1 accuracy of 91.01%, 71.88%, and 69.8% on CIFAR-10, CIFAR-100, and ImageNet, respectively, using the VGG 16 architecture.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"122199861","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-20DOI: 10.1109/ICAIIC57133.2023.10067097
Yeong-Jun Kim, Y. Cho
In this paper, performance of beam-tracking technique is evaluated for cellular systems with an intelligent reflective surface (IRS). A preamble design technique for IRS-assisted cellular systems is proposed using the complex conjugate property of the Zadoff-Chu sequence and reflecting nature of IRS. Moreover, a beam-tracking technique for mmWave cellular systems with a uniform planar array (UPA) is proposed to track the variation in angle of departure (AoD) using sub-panel structures of the UPA. Through simulation, it is demonstrated that the beam tracking technique with the proposed preamble can successfully track 2-D AoD trajectories of the base station and IRS.
{"title":"Performance Analysis of Beam-Tracking Technique for IRS-assisted Cellular Systems","authors":"Yeong-Jun Kim, Y. Cho","doi":"10.1109/ICAIIC57133.2023.10067097","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067097","url":null,"abstract":"In this paper, performance of beam-tracking technique is evaluated for cellular systems with an intelligent reflective surface (IRS). A preamble design technique for IRS-assisted cellular systems is proposed using the complex conjugate property of the Zadoff-Chu sequence and reflecting nature of IRS. Moreover, a beam-tracking technique for mmWave cellular systems with a uniform planar array (UPA) is proposed to track the variation in angle of departure (AoD) using sub-panel structures of the UPA. Through simulation, it is demonstrated that the beam tracking technique with the proposed preamble can successfully track 2-D AoD trajectories of the base station and IRS.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125044677","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-20DOI: 10.1109/ICAIIC57133.2023.10067087
Jiwon Moon, S. Song, Jun-Geol Baek
In the recent manufacturing process, as the introduction of smart factories spreads, high-dimensional data are being collected in real-time from various sensors of production facilities. However, existing anomaly detection models often do not reflect temporal factors, and even if they do, models that reflect temporal information are separately trained, resulting in a problem of falling into local optima. Therefore, it is very difficult to detect process anomalies in real-time by reflecting both correlations between high-dimensional variables and temporary dependency. This study proposes Temporal Encoder with Normalizing Flow (TENF), which can reflect both the correlation between variables and the time dependency in real-time using a relatively simple structure model. TENF consists of a Temporal Encoder for reflecting temporal dependencies and a NF Module for learning the distribution of high-dimensional data and is learned in an end-to-end manner. Experiments on multivariate time series data with similar characteristics to those generated in the manufacturing process demonstrate experimentally superior anomaly detection performance compared to existing models.
{"title":"Multivariate Time Series Anomaly Detection via Temporal Encoder with Normalizing Flow","authors":"Jiwon Moon, S. Song, Jun-Geol Baek","doi":"10.1109/ICAIIC57133.2023.10067087","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067087","url":null,"abstract":"In the recent manufacturing process, as the introduction of smart factories spreads, high-dimensional data are being collected in real-time from various sensors of production facilities. However, existing anomaly detection models often do not reflect temporal factors, and even if they do, models that reflect temporal information are separately trained, resulting in a problem of falling into local optima. Therefore, it is very difficult to detect process anomalies in real-time by reflecting both correlations between high-dimensional variables and temporary dependency. This study proposes Temporal Encoder with Normalizing Flow (TENF), which can reflect both the correlation between variables and the time dependency in real-time using a relatively simple structure model. TENF consists of a Temporal Encoder for reflecting temporal dependencies and a NF Module for learning the distribution of high-dimensional data and is learned in an end-to-end manner. Experiments on multivariate time series data with similar characteristics to those generated in the manufacturing process demonstrate experimentally superior anomaly detection performance compared to existing models.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125669519","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-20DOI: 10.1109/ICAIIC57133.2023.10067012
Ijaz Ahmad, Seokjoo Shin
Lossy image compression provides an efficient solution to the exchange and storage of large volumes of image data for various applications. The main design principle of a lossy compression algorithm is to discard visually insignificant information as much as possible while keeping the resulted visible artifacts at a minimum. However, these unperceivable defects significantly degrade the performance of a trained deep learning (DL) model. Therefore, to improve the classification performance of the models on noisy images, we propose a noise-based data augmentation technique called noise-cuts-noise approach. The simulation analysis have shown that the proposed method efficiently mitigates the performance gap on highly compressed images for example, the accuracy difference is reduced from 11% to 2% for classification of natural images. For uncompressed images, the model performance is either preserved or improved. In addition, to validate the usefulness of the proposed method, we considered a case study of multi-label classification task in chest X-ray (CXR) images. The model accuracy on highly compressed images with the proposed augmentation method increased 2% on higher resolution images while the accuracy difference reduced from 6% to 1% on smaller resolution images.
{"title":"Noise-cuts-Noise Approach for Mitigating the JPEG Distortions in Deep Learning","authors":"Ijaz Ahmad, Seokjoo Shin","doi":"10.1109/ICAIIC57133.2023.10067012","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067012","url":null,"abstract":"Lossy image compression provides an efficient solution to the exchange and storage of large volumes of image data for various applications. The main design principle of a lossy compression algorithm is to discard visually insignificant information as much as possible while keeping the resulted visible artifacts at a minimum. However, these unperceivable defects significantly degrade the performance of a trained deep learning (DL) model. Therefore, to improve the classification performance of the models on noisy images, we propose a noise-based data augmentation technique called noise-cuts-noise approach. The simulation analysis have shown that the proposed method efficiently mitigates the performance gap on highly compressed images for example, the accuracy difference is reduced from 11% to 2% for classification of natural images. For uncompressed images, the model performance is either preserved or improved. In addition, to validate the usefulness of the proposed method, we considered a case study of multi-label classification task in chest X-ray (CXR) images. The model accuracy on highly compressed images with the proposed augmentation method increased 2% on higher resolution images while the accuracy difference reduced from 6% to 1% on smaller resolution images.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130335669","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-20DOI: 10.1109/ICAIIC57133.2023.10067032
Barun Kumar Saha, Luca Haab, D. Tandur
Industry 4.0 has witnessed a widespread use of Artificial Intelligence (AI), which, however, often focuses on the operational aspects. In contrast, the life-cycle of any industrial project begins much earlier. Motivated by this, we present an intent-based approach toward bid engineering. In particular, we consider the use of AI to automatically extract the intended specifications-technical and non-technical-of customers from Requests for Proposals (RFPs) by defining relevant data models. Subsequently, we annotate texts from real-life RFPs to train an AI model. In addition, we also design RfpAnno, an end-to-end solution to annotate documents, train models, and extract specifications as structured data. Experimental results indicate that the AI model has about 85% precision and recall, on average, using the test data set. Overall, RfpAnno can potentially reduce the time and effort required by bid engineers to manually copy requirements from RFPs.
{"title":"A Natural Language Understanding Approach Toward Extraction of Specifications from Request for Proposals","authors":"Barun Kumar Saha, Luca Haab, D. Tandur","doi":"10.1109/ICAIIC57133.2023.10067032","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067032","url":null,"abstract":"Industry 4.0 has witnessed a widespread use of Artificial Intelligence (AI), which, however, often focuses on the operational aspects. In contrast, the life-cycle of any industrial project begins much earlier. Motivated by this, we present an intent-based approach toward bid engineering. In particular, we consider the use of AI to automatically extract the intended specifications-technical and non-technical-of customers from Requests for Proposals (RFPs) by defining relevant data models. Subsequently, we annotate texts from real-life RFPs to train an AI model. In addition, we also design RfpAnno, an end-to-end solution to annotate documents, train models, and extract specifications as structured data. Experimental results indicate that the AI model has about 85% precision and recall, on average, using the test data set. Overall, RfpAnno can potentially reduce the time and effort required by bid engineers to manually copy requirements from RFPs.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126381885","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-20DOI: 10.1109/ICAIIC57133.2023.10067047
Soha Lee, Hyeyoung Park
The error backpropagation algorithm is a representative learning method that has been used in most deep network models. However, the error backpropagation algorithm, despite its decent performance, clearly has limits to its biological plausibility. Unlike the learning mechanism of the actual brain, the error backpropagation algorithm must reuse the weights used in the forward calculation for the backward error propagation. In order to overcome these limitations, the feedback alignment method, which uses a fixed random weight for the backpropagation computation, was proposed. The feedback alignment algorithm showed performances comparable to the original error backpropagation on several benchmark data sets. However, it is still in the preliminary stage of analysis, and various analysis on its learning behavior and practical efficiency are needed. In this paper, we combine feedback alignment learning method with popular optimization techniques such as RMSprop and Adam, and investigate its effect on the learning performances through computational experiments on benchmark data sets.
{"title":"Effect of Optimization Techniques on Feedback Alignment Learning of Neural Networks","authors":"Soha Lee, Hyeyoung Park","doi":"10.1109/ICAIIC57133.2023.10067047","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067047","url":null,"abstract":"The error backpropagation algorithm is a representative learning method that has been used in most deep network models. However, the error backpropagation algorithm, despite its decent performance, clearly has limits to its biological plausibility. Unlike the learning mechanism of the actual brain, the error backpropagation algorithm must reuse the weights used in the forward calculation for the backward error propagation. In order to overcome these limitations, the feedback alignment method, which uses a fixed random weight for the backpropagation computation, was proposed. The feedback alignment algorithm showed performances comparable to the original error backpropagation on several benchmark data sets. However, it is still in the preliminary stage of analysis, and various analysis on its learning behavior and practical efficiency are needed. In this paper, we combine feedback alignment learning method with popular optimization techniques such as RMSprop and Adam, and investigate its effect on the learning performances through computational experiments on benchmark data sets.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117073578","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-20DOI: 10.1109/ICAIIC57133.2023.10067063
Song-Min Lee, Junho Kwak, Jeong-Keun Cho
Embedded software for vehicles is becoming increasingly complex and huge, and the complexity of test evaluations and the amount of test cases are increasing exponentially. The hardware-based testing methods currently in use often involve some complex preparation and scheduling, making it difficult to determine the actual test results. Therefore, to build a hardware-independent test environment, this paper proposes a method to insert and test components into the port interface for communication between software components of AUTOSAR. This provides a test environment that can be quickly removed to verify the operation of automotive software and helps improve quality by quickly and easily checking errors not only in the software production process but also in the completed system.
{"title":"Preliminary Design for Development of Detachable Test Automation System Based on AUTOSAR","authors":"Song-Min Lee, Junho Kwak, Jeong-Keun Cho","doi":"10.1109/ICAIIC57133.2023.10067063","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10067063","url":null,"abstract":"Embedded software for vehicles is becoming increasingly complex and huge, and the complexity of test evaluations and the amount of test cases are increasing exponentially. The hardware-based testing methods currently in use often involve some complex preparation and scheduling, making it difficult to determine the actual test results. Therefore, to build a hardware-independent test environment, this paper proposes a method to insert and test components into the port interface for communication between software components of AUTOSAR. This provides a test environment that can be quickly removed to verify the operation of automotive software and helps improve quality by quickly and easily checking errors not only in the software production process but also in the completed system.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"117254446","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2023-02-20DOI: 10.1109/ICAIIC57133.2023.10066996
Yonghun Lee, Daejin Park
As design complexity increases, turn-around time (TAT) of design development increases. Designers may not have enough time to cover all test, because Verilog simulation time increases. The aim of this paper is to present an existing Verilog simulation method and to propose a new method to reduce simulation run time for the design of large system implemented in Verilog in the iterative flows. Small changes in testbench caused the need to repeat all design flows, including basic and common test sequences such as booting and power on stabilization sequences. The proposed verification flows use the Tcl based verification code for dynamically reloading from previous simulation snapshot without repeated compiling of source code. The basic and commonly used long test sequences are saved by simulator using Tcl command and reload the saved snapshot after driving the test sequence using Tcl code without recompiling. The total simulation time was reduced by 53% with the proposed verification flow.
{"title":"Fast Verilog Simulation using Tel-based Verification Code Generation for Dynamically Reloading from Pre-Simulation Snapshot","authors":"Yonghun Lee, Daejin Park","doi":"10.1109/ICAIIC57133.2023.10066996","DOIUrl":"https://doi.org/10.1109/ICAIIC57133.2023.10066996","url":null,"abstract":"As design complexity increases, turn-around time (TAT) of design development increases. Designers may not have enough time to cover all test, because Verilog simulation time increases. The aim of this paper is to present an existing Verilog simulation method and to propose a new method to reduce simulation run time for the design of large system implemented in Verilog in the iterative flows. Small changes in testbench caused the need to repeat all design flows, including basic and common test sequences such as booting and power on stabilization sequences. The proposed verification flows use the Tcl based verification code for dynamically reloading from previous simulation snapshot without repeated compiling of source code. The basic and commonly used long test sequences are saved by simulator using Tcl command and reload the saved snapshot after driving the test sequence using Tcl code without recompiling. The total simulation time was reduced by 53% with the proposed verification flow.","PeriodicalId":105769,"journal":{"name":"2023 International Conference on Artificial Intelligence in Information and Communication (ICAIIC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-02-20","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124310507","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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