ReActNet-XGBoost 硬體加速器設計與實現——資源受限場景的應用探索

以作者查詢圖書館館藏

、以作者查詢臺灣博碩士

、以作者查詢全國書目

、勘誤回報

、線上人數：12

、訪客IP：3.15.148.76

姓名

蕭如珊(Ju-Shan Hsiao) 查詢紙本館藏

畢業系所

資訊工程學系

論文名稱

ReActNet-XGBoost 硬體加速器設計與實現——資源受限場景的應用探索
(ReActNet-XGBoost Hardware Accelerator: Design, Implementation, and Application Exploration in Resource-Constrained Scenarios)

相關論文

★ 整合GRAFCET虛擬機器的智慧型控制器開發平台	★ 分散式工業電子看板網路系統設計與實作
★ 設計與實作一個基於雙攝影機視覺系統的雙點觸控螢幕	★ 智慧型機器人的嵌入式計算平台
★ 一個即時移動物偵測與追蹤的嵌入式系統	★ 一個固態硬碟的多處理器架構與分散式控制演算法
★ 基於立體視覺手勢辨識的人機互動系統	★ 整合仿生智慧行為控制的機器人系統晶片設計
★ 嵌入式無線影像感測網路的設計與實作	★ 以雙核心處理器為基礎之車牌辨識系統
★ 基於立體視覺的連續三維手勢辨識	★ 微型、超低功耗無線感測網路控制器設計與硬體實作
★ 串流影像之即時人臉偵測、追蹤與辨識─嵌入式系統設計	★ 一個快速立體視覺系統的嵌入式硬體設計
★ 即時連續影像接合系統設計與實作	★ 基於雙核心平台的嵌入式步態辨識系統

檔案

[Endnote RIS 格式]

[Bibtex 格式]

[相關文章]

[文章引用]

[完整記錄]

[館藏目錄]

至系統瀏覽論文 (2030-1-21以後開放)

摘要(中)

隨著工業 4.0 和邊緣運算技術的快速發展，智慧醫療與工業監控等場景對
即時資料處理和智慧化分析提出了高效能、低功耗與資源優化的嚴苛需求。然而，傳統卷積神經網路 (CNN) 因其高運算量與記憶體需求，難以滿足資源受限邊緣裝置的應用場景。為解決此一挑戰，本研究提出了一種結合 ReActNet 架構與 XGBoost 分類器的硬體加速器，專注於一維時間序列資料的處理。
本設計以 1D CNN 的輕量化特性為基礎，通過二值卷積技術顯著降低運算
負擔，嘗試取得低功耗與高準確度之間的平衡，並且為智慧醫療中的生理訊號監測以及工業監控中的設備異常檢測提供了創新的解決方案，展現出應用於資源受限邊緣裝置的潛力。

摘要(英)

With the rapid advancement of Industry 4.0 and edge computing technologies, applications in smart healthcare and industrial monitoring demand highly efficient, low-power, and resource-optimized solutions for real-time data processing and intelligent analysis. However, traditional convolutional neural networks (CNNs), with their significant computational and memory requirements, struggle to meet the constraints of resource-limited edge devices. Addressing these challenges, this study introduces a hardware accelerator combining the ReActNet architecture with an XGBoost classifier, specifically designed for processing one-dimensional time-series data.
Built upon the lightweight characteristics of 1D CNNs, the proposed design leverages binary convolution techniques to significantly reduce computational overhead, aiming to achieve an optimal balance between low power consumption and high accuracy. This innovative approach offers promising solutions for real-time physiological signal monitoring in smart healthcare and anomaly detection in industrial monitoring, demonstrating substantial potential for deployment in resource-constrained edge devices.

關鍵字(中)

★ 二值卷積神經網路
★ 硬體加速器
★ 邊緣裝置
★ 資源受限應用

關鍵字(英)

★ Binary Convolutional Neural Network
★ Hardware Accelerator
★ Edge Devices
★ Resource-Constrained Applications

論文目次

摘要 i
Abstract ii
致謝 iii
目錄 v
圖目錄 viii
表目錄 x
第一章、緒論 1
1.1 研究背景 1
1.2 研究目標 3
1.3 論文架構 4
第二章、技術回顧 6
2.1 二值卷積神經網路 6
2.1.1 Binarized Neural Network 6
2.1.2 XNOR-Net 8
2.1.3 ReActNet 11
2.2 XGBoost 15
2.2.1 多決策樹硬體加速器 18
2.3 MIAT 系統設計方法論 21
2.3.1 IDEF0 階層式模組化設計 22
2.3.2 GRAFCET 離散事件建模 24
2.3.3 硬體高階合成 27
第三章、ReActNet-XGBoost 硬體加速器系統設計 30
3.1 系統架構 30
3.1.1 IDEF0 階層式模組化設計 31
3.1.2 GRAFCET 離散事件建模 32
3.2 ReActNet 特徵提取硬體模組 33
3.2.1 IDEF0 階層式模組化設計 34
3.2.2 GRAFCET 離散事件建模 34
3.3 XGBoost 分類器硬體模組 41
3.3.1 XGBoost 硬體化工具 41
3.3.2 IDEF0 階層式模組化設計 47
3.3.3 GRAFCET 離散事件建模 49
第四章、實驗結果 54
4.1 實驗軟硬體開發環境 54
4.2 實驗說明 55
4.2.1 實驗資料集 56
4.2.2 實驗細節補充說明 57
4.3 ModelSim 波形模擬驗證 57
第五章、結論與未來展望 60
5.1 結論 60
5.2 未來展望 60
參考文獻 62

參考文獻

[1] T. Kalsoom, S. Ahmed, P. M. Rafi-ul-Shan, M. Azmat, P. Akhtar, Z. Pervez, M. A. Imran, and M. Ur-Rehman, "Impact of IoT on Manufacturing Industry 4.0: A New Triangular Systematic Review," Sustainability, vol. 13, no. 22, p.12506, 2021.
[2] T.-W. Sung, P.-w. Tsai, T. Gaber, and C.-Y. Lee, "Artificial Intelligence of Things (AIoT) Technologies and Applications," Wirel. Commun. Mob. Comput., vol. 2021, pp. 9781271:1-9781271:2, 2021.
[3] S. Soomro, M. H. Miraz, A. Prasanth, and M. Abdullah, "Artificial intelligence enabled IoT: Traffic congestion reduction in smart cities," in Smart Cities Symposium 2018, pp. 1-6, 2018.
[4] I. Rojek and J. Studzinski, "Detection and Localization of Water Leaks in Water Nets Supported by an ICT System with Artificial Intelligence Methods as a Way Forward for Smart Cities," Sustainability, vol. 11, no. 2, p. 518, 2019.
[5] C. Stolojescu-Crisan, C. Crisan, and B.-P. Butunoi, "An IoT-Based Smart Home Automation System," Sensors, vol. 21, no. 11, p. 3784, 2021.
[6] A. Koubaa, A. Aldawood, B. Saeed, A. Hadid, M. Ahmed, A. Saad, H. Alkhouja, A. Ammar, and M. Alkanhal, "Smart Palm: An IoT Framework for Red Palm Weevil Early Detection," Agronomy, vol. 10, no. 7, p. 987, 2020.
[7] M. J. O′Grady, D. Langton, and G. M. P. O′Hare, "Edge computing: A tractable model for smart agriculture?," Artificial Intelligence in Agriculture, vol. 3, pp. 42-51, 2019/09/01/ 2019.
[8] A. W. Sardar, F. Ullah, J. Bacha, J. Khan, F. Ali, and S. Lee, "Mobile sensors based platform of Human Physical Activities Recognition for COVID-19 spread minimization," Computers in Biology and Medicine, vol. 146, p. 105662, 2022/07/01/ 2022.
[9] W. Li, Y. Chai, F. Khan, S. R. U. Jan, S. Verma, V. G. Menon, Kavita, and X. Li, "A Comprehensive Survey on Machine Learning-Based Big Data Analytics for IoT-Enabled Smart Healthcare System," Mobile Networks and Applications, vol. 26, no. 1, pp. 234-252, 2021/02/01 2021.
[10] M.-Y. Lin, C.-H. Chen, Z.-B. Dong, and C.-C. Chen, "Gigabit Modbus user datagram protocol fieldbus network integrated with industrial vision communication,"
Microprocessors and Microsystems, vol. 94, p. 104682, 2022/10/01/ 2022.
[11] J. Wang, D. Wang, S. Wang, W. Li, and K. Song, "Fault Diagnosis of Bearings Based on Multi-Sensor Information Fusion and 2D Convolutional Neural Network," IEEE Access, vol. 9, pp. 23717-23725, 2021.
[12] R. Zanc, T. Cioara, and I. Anghel, "Forecasting Financial Markets using Deep Learning," 2019 IEEE 15th International Conference on Intelligent Computer Communication and Processing (ICCP), pp. 459-466, 2019.
[13] A. K. Arslan, ?. Ya?ar, and C. Colak, "An Intelligent System for the Classification of Lung Cancer Based on Deep Learning Strategy," 2019 International Artificial Intelligence and Data Processing Symposium (IDAP), pp. 1-4, 2019.
[14] D. Zhang and S. Liu, "Top-Down Saliency Object Localization Based on Deep-Learned Features," 2018 11th International Congress on Image and Signal Processing, BioMedical Engineering and Informatics (CISP-BMEI), pp. 1-9, 2018.
[15] J. Kim, S. Chang, and N. Kwak, "PQK: model compression via pruning, quantization, and knowledge distillation," arXiv preprint arXiv:2106.14681, 2021.
[16] C. Rudin, "Stop explaining black box machine learning models for high stakes decisions and use interpretable models instead," Nature Machine Intelligence, vol. 1, no. 5, pp. 206-215, 2019/05/01 2019.
[17] C. Yue and N. K. Jha, "Learning Interpretable Differentiable Logic Networks," IEEE Transactions on Circuits and Systems for Artificial Intelligence, 2024.
[18] S. Kiranyaz, O. Avci, O. Abdeljaber, T. Ince, M. Gabbouj, and D. J. Inman, "1D convolutional neural networks and applications: A survey," Mechanical systems and signal processing, vol. 151, p. 107398, 2021.
[19] J. Lu, D. Liu, Z. Liu, X. Cheng, L. Wei, C. Zhang, X. Zou, and B. Liu, "Efficient Hardware Architecture of Convolutional Neural Network for ECG Classification in Wearable Healthcare Device," IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 68, no. 7, pp. 2976-2985, 2021.
[20] Z. Liu, Z. Shen, M. Savvides, and K.-T. Cheng, "Reactnet: Towards precise binary neural network with generalized activation functions," in Computer Vision–ECCV 2020: 16th European Conference, Glasgow, UK, August 23–28, 2020, Proceedings, Part XIV 16, pp. 143-159, 2020.
[21] T. Chen and C. Guestrin, "Xgboost: A scalable tree boosting system," in Proceedings of the 22nd acm sigkdd international conference on knowledge discovery and data mining, pp. 785-794, 2016.
[22] M. Courbariaux, Y. Bengio, and J.-P. David, "Binaryconnect: Training deep neural networks with binary weights during propagations," Advances in neural information processing systems, vol. 28, 2015.
[23] I. Hubara, M. Courbariaux, D. Soudry, R. El-Yaniv, and Y. Bengio, "Binarized neural networks," Advances in neural information processing systems, vol. 29, 2016.
[24] Y. LeCun, L. Bottou, Y. Bengio, and P. Haffner, "Gradient-based learning applied to document recognition," Proceedings of the IEEE, vol. 86, no. 11, pp. 2278-2324, 1998.
[25] A. Krizhevsky and G. Hinton, "Learning multiple layers of features from tiny images," 2009.
[26] M. Rastegari, V. Ordonez, J. Redmon, and A. Farhadi, "Xnor-net: Imagenet classification using binary convolutional neural networks," in European conference on computer vision, pp. 525-542, 2016.
[27] A. G. Howard, M. Zhu, B. Chen, D. Kalenichenko, W. Wang, T. Weyand, M. Andreetto, and H. Adam, "Mobilenets: Efficient convolutional neural networks for mobile vision applications," arXiv preprint arXiv:1704.04861, 2017.
[28] J. H. Friedman, "Greedy function approximation: a gradient boosting machine," Annals of statistics, pp. 1189-1232, 2001.
[29] Y. Zhang and A. Haghani, "A gradient boosting method to improve travel time prediction," Transportation Research Part C: Emerging Technologies, vol. 58, pp. 308-324, 2015.
[30] A. Alcolea and J. Resano, "FPGA accelerator for gradient boosting decision trees," in Electronics vol. 10, ed, p. 314, 2021.
[31] C.-H. Chen, M.-Y. Lin, and X.-C. Guo, "High-level modeling and synthesis of smart sensor networks for Industrial Internet of Things," Computers & Electrical Engineering, vol. 61, pp. 48-66, 2017/07/01/ 2017.
[32] P. H. Chu and C. H. Chen, "ReActXGB: A Hybrid Binary Convolutional Neural Network Architecture for Improved Performance and Computational Efficiency," in 2024 International Conference on Consumer Electronics - Taiwan (ICCE-Taiwan), pp. 327-328, 2024.
[33] S. Fazeli, "ECG Heartbeat Categorization Dataset", Kaggle, https://www.kaggle.com/datasets/shayanfazeli/heartbeat, 2018, (accessed Jan. 2025)

指導教授

陳慶瀚(Ching-Han Chen)

審核日期

2025-1-21

推文