2022 IEEE 15th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)最新文献_第2页

Intelligent congestion control for NoC architecture in Gem5 simulator Gem5仿真器中NoC架构的智能拥塞控制

2022 IEEE 15th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) Pub Date : 2022-12-01 DOI: 10.1109/MCSoC57363.2022.00062

Smriti Srivastava, M. Shaikh, G. Shivaneetha, Minal Moharir

{"title":"Intelligent congestion control for NoC architecture in Gem5 simulator","authors":"Smriti Srivastava, M. Shaikh, G. Shivaneetha, Minal Moharir","doi":"10.1109/MCSoC57363.2022.00062","DOIUrl":"https://doi.org/10.1109/MCSoC57363.2022.00062","url":null,"abstract":"Congestion in a network significantly impacts the performance of an NoC as there is a substantial increase in latency and power consumption. Machine Learning techniques aid in designing routing methods to keep the network cognizant of the traffic status. This paper presents a congestion-aware Q-routing algorithm based on the Q-learning model of reinforcement learning. The proposed algorithm enhances the network's performance in an NoC under heavy traffic conditions by routing the packets along a less congested path. Thus, it reduces the congestion in the network. This is possible as Q-learning allows the network to keep track of the local and non-local congestion by estimating Q-values. The Q-values guide a node in sending a data packet along an optimal path, thereby evading busy routes. The simulation done on the gem5 simulator with uniform link latency in the network exhibits that Q-routing performs better in a high-load environment than traditional XY and Odd-Even Routing methods, with a performance gain of 5.73% and 12.73%, respectively. The results for varied link latencies that were randomly assigned to create a practical congestion-probable scenario showed that the proposed method outperformed both the XY and Odd-Even routing algorithm with a respective performance gain of 7.38% and 15.19%.","PeriodicalId":150801,"journal":{"name":"2022 IEEE 15th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132131733","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}

引用次数: 1

Implementation of Edge-cloud Cooperative CNN Inference on an IoT Platform 边缘云协同CNN推理在物联网平台上的实现

2022 IEEE 15th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) Pub Date : 2022-12-01 DOI: 10.1109/MCSoC57363.2022.00060

Yuan Wang, H. Shibamura, KuanYi Ng, Koji Inoue

引用次数: 2

Algorithm to Interconvert SQL and Procedural Visual Queries SQL和过程可视化查询的相互转换算法

2022 IEEE 15th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) Pub Date : 2022-12-01 DOI: 10.1109/MCSoC57363.2022.00048

Tomonori Suzuki, Y. Watanobe, Divij G. Singh

引用次数: 0

Online Image Sensor Fault Detection for Autonomous Vehicles 自动驾驶汽车在线图像传感器故障检测

2022 IEEE 15th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) Pub Date : 2022-12-01 DOI: 10.1109/MCSoC57363.2022.00028

Yizhi Chen, Wenyao Zhu, Dejiu Chen, Zhonghai Lu

{"title":"Online Image Sensor Fault Detection for Autonomous Vehicles","authors":"Yizhi Chen, Wenyao Zhu, Dejiu Chen, Zhonghai Lu","doi":"10.1109/MCSoC57363.2022.00028","DOIUrl":"https://doi.org/10.1109/MCSoC57363.2022.00028","url":null,"abstract":"Automated driving vehicles have shown glorious potential in the near future market due to the high safety and convenience for drivers and passengers. Image sensors' reliability attract many researchers' interests as many image sensors are used in autonomous vehicles. We propose an online image sensor fault detection method based on comparing the historical variances of normal pixels and defective pixels to detect faults. For fault pixels without uncertainty, with a detecting window of more than 30 frames, we get 100% accuracy and 100% recall on realistic continuous traffic pictures from the KITTI data set. We also explore the influence of fault pixel values' uncertainty from 0% to 25% and study different fixed thresholds and a dynamic threshold for judgments. Strict threshold, which is 0.1, has a high accuracy (99.16%) but has a low recall (34.46%) for 15% uncertainty. Loose threshold, which is 0.3, has a relatively high recall (83.78%) but mistakes too many normal pixels with 18.17% accuracy for 15% uncertainty. Our dynamic threshold balances the accuracy and recall. It gets 100% accuracy and 58.69% recall for 5% uncertainty and 78.38% accuracy and 55.39% recall for 15% uncertainty. Based on the detected damage pixel rate, we develop a health score for evaluating the image sensor system intuitively. It can also be helpful for making decision about replacing cameras.","PeriodicalId":150801,"journal":{"name":"2022 IEEE 15th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129503872","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}

引用次数: 0

Movie Oriented Positive Negative Emotion Classification from EEG Signal using Wavelet transformation and Machine learning Approaches 基于小波变换和机器学习的脑电信号正向消极情绪分类

2022 IEEE 15th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) Pub Date : 2022-12-01 DOI: 10.1109/MCSoC57363.2022.00014

Abu Saleh Musa Miah, Jungpil Shin, Md. Al Mehedi Hasan, M. I. Molla, Y. Okuyama, Yoichi Tomioka

{"title":"Movie Oriented Positive Negative Emotion Classification from EEG Signal using Wavelet transformation and Machine learning Approaches","authors":"Abu Saleh Musa Miah, Jungpil Shin, Md. Al Mehedi Hasan, M. I. Molla, Y. Okuyama, Yoichi Tomioka","doi":"10.1109/MCSoC57363.2022.00014","DOIUrl":"https://doi.org/10.1109/MCSoC57363.2022.00014","url":null,"abstract":"Electroencephalography (EEG) sensor plays an important role in developing brain-computer interfaces (BCI) to enhance human-computer interaction (HCI). Nowadays, various types of research works are performed to develop EEG-based HCI systems for controlling and monitoring systems. However, researchers are still facing challenges in developing this system due to noise from the physiological and internal and external artefacts. This study proposed a method to find useful electrodes and extract potential information from the brain nerves for the classification of positive or negative emotions. The collected emotion's EEG signal is recorded using 14 electrodes from the 30-younger people. Two movies were used for positive and negative emotions. In the proposed method, we first extracted the five bands wavelet transform from the EEG and then calculated the standard deviation (SD), average power (AVP) and mean absolute value (MAV) of the five bands wavelet information. Finally, we applied an extra tree classifier (ETC), random forest (RF), and support vector machine (SVM) to classify the emotion based on the feature vector. Among three classifiers ETC achieved higher performance accuracy in F3, FC5, T8, FC6, F8, and AF4 electrodes. This indicates that the F3, FC5, T8, FC6, F8, and AF4 electrodes carry potential information in positive-negative emotion classification.","PeriodicalId":150801,"journal":{"name":"2022 IEEE 15th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"116442959","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}

引用次数: 1

High-Performance Asynchronous CNN Accelerator with Early Termination 高性能异步CNN加速器与早期终止

2022 IEEE 15th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) Pub Date : 2022-12-01 DOI: 10.1109/MCSoC57363.2022.00031

Tan Rong Loo, T. Teo, Mulat Ayinet Tiruye, I-Chyn Wey

{"title":"High-Performance Asynchronous CNN Accelerator with Early Termination","authors":"Tan Rong Loo, T. Teo, Mulat Ayinet Tiruye, I-Chyn Wey","doi":"10.1109/MCSoC57363.2022.00031","DOIUrl":"https://doi.org/10.1109/MCSoC57363.2022.00031","url":null,"abstract":"Convolutional Neural Network (CNN), especially very deep networks, are highly computation intensive, resulting in long delays and high-power consumption. The dynamically varying environmental conditions in real world inference can result in highly complex problems, and hence the need for these inefficient deep networks to guarantee satisfactory accuracy all the time. Several studies employ approximation techniques to execute partial computation of the network, in attempt to reduce the amount of computation were unnecessary. However, such approaches are still highly sequential in nature, since they still need to run the whole network. This paper proposes an early termination architecture on an already-trained CNN to allow for testing the partial results midway through the network, reducing computations by terminating the main network when it is sufficient as the inference results. The first proposal is implemented in synchronous circuit, however, due to its nature all memory elements are required to capture even when no new data is generated. The second proposal employs the use of asynchronous circuit to significantly reducing power consumption and further sped up the architecture since an operation need not wait the slowest critical path in the circuit. The proposed circuits were designed on FPGA platform. The results of the asynchronous circuit show a nearly 20% increment in speed with about 12% reduction in power consumption in comparison with a synchronous circuit.","PeriodicalId":150801,"journal":{"name":"2022 IEEE 15th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"132616390","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}

引用次数: 0

Using scheduling entropy amplification in CUDA/OpenMP code to exhibit non-reproducibility issues 在CUDA/OpenMP代码中使用调度熵放大来显示不可再现性问题

2022 IEEE 15th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) Pub Date : 2022-12-01 DOI: 10.1109/MCSoC57363.2022.00040

D. Defour

引用次数: 0

Critical Signature Assertion and On-the-Fly Recovery for Control Flow Errors in Processors 处理器控制流错误的关键签名断言与动态恢复

2022 IEEE 15th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) Pub Date : 2022-12-01 DOI: 10.1109/MCSoC57363.2022.00052

Ing-Jer Huang, Yi-Ju Ke, Shih-Jung Pao

引用次数: 1

Efficient and High-Performance Sparse Matrix-Vector Multiplication on a Many-Core Array 基于多核阵列的高效、高性能稀疏矩阵向量乘法

2022 IEEE 15th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) Pub Date : 2022-12-01 DOI: 10.1109/MCSoC57363.2022.00038

Peiyao Shi, Aaron Stillmaker, B. Baas

引用次数: 0

A Realization of IO Physical Memory Protection for RISC-V Systems RISC-V系统IO物理内存保护的实现

2022 IEEE 15th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC) Pub Date : 2022-12-01 DOI: 10.1109/MCSoC57363.2022.00066

Jien Hau Ng, Chee Hong Ang, Hwa Chaw Law

{"title":"A Realization of IO Physical Memory Protection for RISC-V Systems","authors":"Jien Hau Ng, Chee Hong Ang, Hwa Chaw Law","doi":"10.1109/MCSoC57363.2022.00066","DOIUrl":"https://doi.org/10.1109/MCSoC57363.2022.00066","url":null,"abstract":"Physical memories or RAMs are essential components in a computer system to hold temporary information required for both software and hardware to work properly. When a system's security is compromised (e.g., due to a malicious application), sensitive information being held in the memories can be leaked out for example to “the cloud”. The RISC-V privileged architecture standard adopts a method called Physical Memory Protection (PMP) to segregate a system's memory into regions with different policy and permissions to prevent unprivileged software from accessing unauthorized regions. However, PMP does not prevent malicious software from hijacking an Input/Output (IO) device with Direct Memory Access (DMA) capability to indirectly gain unauthorized accesses and hence, a similar method commonly termed as “IOPMP” is being worked on in the RISC-V community. This paper describes an early implementation of IOPMP and how it is used to protect physical memory regions in a RISC-V system. Then, the potential performance impact of IOPMP is briefly elaborated. There are still work to be done and this early IOPMP implementation allows various aspects of the protection method such as its scalability, practicality, and effectiveness etc. to be studied for future enhancement.","PeriodicalId":150801,"journal":{"name":"2022 IEEE 15th International Symposium on Embedded Multicore/Many-core Systems-on-Chip (MCSoC)","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"125517505","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}

引用次数: 0