請用此 Handle URI 來引用此文件:
http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/89117
完整後設資料紀錄
DC 欄位 | 值 | 語言 |
---|---|---|
dc.contributor.advisor | 李建模 | zh_TW |
dc.contributor.advisor | James Chien-Mo Li | en |
dc.contributor.author | 梁哲嘉 | zh_TW |
dc.contributor.author | Zhe-Jia Liang | en |
dc.date.accessioned | 2023-08-16T17:12:14Z | - |
dc.date.available | 2023-11-09 | - |
dc.date.copyright | 2023-08-16 | - |
dc.date.issued | 2023 | - |
dc.date.submitted | 2023-08-06 | - |
dc.identifier.citation | Too Hot to Test workshop Intel, 2021, [online] Available: https://youtu.be/0gPSbZqbXUg.
K. M. Butler, J. Saxena, A. Jain, T. Fryars, J. Lewis and G. Hetherington, "Minimizing power consumption in scan testing: pattern generation and DFT techniques," 2004 International Conferce on Test, Charlotte, NC, USA, 2004, pp. 355-364. S. Remersaro, X. Lin, Z. Zhang, S. M. Reddy, I. Pomeranz and J. Rajski, "Preferred Fill: A Scalable Method to Reduce Capture Power for Scan Based Designs," 2006 IEEE International Test Conference, Santa Clara, CA, USA, 2006, pp. 1-10. K. Abdel-Hafez et al., "Comprehensive Power-Aware ATPG Methodology for Complex Low-Power Designs," 2022 IEEE International Test Conference (ITC), Anaheim, CA, USA, 2022, pp. 334-339. A. Kumar et al., "ML-based Fast On-Chip Transient Thermal Simulation for Heterogeneous 2.5D/3D IC Designs," 2022 International Symposium on VLSI Design, Automation and Test (VLSI-DAT), Hsinchu, Taiwan, 2022, pp. 1-8. Y. Zhou, H. Ren, Y. Zhang, B. Keller, B. Khailany and Z. Zhang, "PRIMAL: Power Inference using Machine Learning," 2019 56th ACM/IEEE Design Automation Conference (DAC), Las Vegas, NV, USA, 2019, pp. 1-6. H. Dhotre, S. Eggersglüß, K. Chakrabarty and R. Drechsler, "Machine Learning-based Prediction of Test Power," 2019 IEEE European Test Symposium (ETS), Baden-Baden, Germany, 2019, pp. 1-6. J. Wen et al., "DNN-based Fast Static On-chip Thermal Solver," 2020 36th Semiconductor Thermal Measurement, Modeling & Management Symposium (SEMI-THERM), 2020, pp. 65-75. Goodfellow, I. J., et al. "Generative Adversarial Networks, 1–9." arXiv preprint arXiv:1406.2661 (2014). W. Jin, S. Sadiqbatcha, J. Zhang and S. X. . -D. Tan, "Full-Chip Thermal Map Estimation for Commercial Multi-Core CPUs with Generative Adversarial Learning," 2020 IEEE/ACM International Conference On Computer Aided Design (ICCAD), San Diego, CA, USA, 2020, pp. 1-9. V. A. Chhabria, V. Ahuja, A. Prabhu, N. Patil, P. Jain and S. S. Sapatnekar, "Thermal and IR Drop Analysis Using Convolutional Encoder-Decoder Networks," 2021 26th Asia and South Pacific Design Automation Conference (ASP-DAC), Tokyo, Japan, 2021, pp. 690-696. Krizhevsky, Alex, Ilya Sutskever, and Geoffrey E. Hinton. "Imagenet classification with deep convolutional neural networks." Communications of the ACM 60.6 (2017): 84-90. J. -X. Chen et al., "Vector-based Dynamic IR-drop Prediction Using Machine Learning," 2022 27th Asia and South Pacific Design Automation Conference (ASP-DAC), Taipei, Taiwan, 2022, pp. 202-207. R. Sankaralingam, R. R. Oruganti and N. A. Touba, "Static compaction techniques to control scan vector power dissipation," Proceedings 18th IEEE VLSI Test Symposium, Montreal, QC, Canada, 2000, pp. 35-40. O. Ronneberger, P. Fischer, and T. Brox, ‘‘U-Net: Convolutional networks for biomedical image segmentation,’’ in Proc. Int. Conf. Med. Image Comput. Comput.-Assist. Intervent., 2015, pp. 234–241. Maas, Andrew L., Awni Y. Hannun, and Andrew Y. Ng. "Rectifier nonlinearities improve neural network acoustic models." Proc. icml. Vol. 30. No. 1. 2013. Xu, Bing et al., "Empirical evaluation of rectified activations in convolutional network." arXiv preprint arXiv:1505.00853 (2015). Dumoulin, Vincent, and Francesco Visin. "A guide to convolution arithmetic for deep learning." arXiv preprint arXiv:1603.07285 (2016). J. Hennessy, N. Jouppi, F. Baskett, and J. Gill, “MIPS: a VLSI processor architecture,” in VLSI Systems and Computations. Springer, 1981, pp. 337–346. Gaisler home page, 2004, [Online]. Available: https://www.gaisler.com/index.php NanGate FreePDK45 open cell library, Jan. 2016, [Online]. Available: http://www.nangate.com/?page_id=2325 Ansys Inc. RedHawk-SC User Manual, 2021. Wei Huang et al., "HotSpot: a compact thermal modeling methodology for early-stage VLSI design," in IEEE Transactions on Very Large Scale Integration (VLSI) Systems, vol. 14, no. 5, pp. 501-513, May 2006. | - |
dc.identifier.uri | http://tdr.lib.ntu.edu.tw/jspui/handle/123456789/89117 | - |
dc.description.abstract | 測試晶片時的高功率會對測試中的晶片造成熱損傷。因此我們需要進行功率分析和溫度分析以確保自動測試圖樣產生時的熱安全性。因為自動測試圖樣由許多的週期(cycle)組成,我們需要大量的執行時間以及大量的硬碟儲存空間來完成前述的分析。在本論文中,我們針對晶片測試,提出功率預測和溫度預測的方法。為了節省預測所需的執行時間,我們使用多個機器學習模型進行功率預測,以及使用 decay surface model進行溫度預測。為了節省硬碟儲存空間,我們使用正反器的邏輯值來建立機器學習模型的輸入特徵,因此我們不需要執行閘位準邏輯模擬來獲得組合電路的邏輯值。對於功率預測,我們的平均絕對百分比誤差(MAPE)小於百分之八。對於溫度預測,我們的平均絕對誤差(MAE)小於攝氏1.2度。本論文使大量的自動測試圖樣之瞬態溫度分析變得可行。對比傳統方法,我們的執行速度達到了75倍的加速,我們所需的硬碟儲存空間則減少了118倍。我們的功率預測及溫度預測可被用於不同的測試速度。因此我們的技術可在最佳化測試時間的同時,確保測試時的熱安全性。 | zh_TW |
dc.description.abstract | High test power causes thermal damage to chips under test. We need power and thermal analyses to ensure thermal safety of ATPG patterns. This requires long runtime and large disk storage because there are many cycles in ATPG patterns. In this thesis, we propose power and thermal predictions for test applications. To save runtime, we use multiple ML models and decay surface models for power and thermal predictions, respectively. To save storage, we build features from flip-flop values, so we don’t need internal logic values from gate-level simulation. Our mean absolute percentage error (MAPE) for power prediction is less than 8%. Our mean absolute error (MAE) for thermal prediction is less than 1.2℃. We enable transient thermal analysis of long ATPG patterns, with 75X runtime speedup and 118X storage reduction. Our predictions are scalable with test speed, so they can be used to optimize test time while ensuring thermal safety. | en |
dc.description.provenance | Submitted by admin ntu (admin@lib.ntu.edu.tw) on 2023-08-16T17:12:14Z No. of bitstreams: 0 | en |
dc.description.provenance | Made available in DSpace on 2023-08-16T17:12:14Z (GMT). No. of bitstreams: 0 | en |
dc.description.tableofcontents | 致謝 i
摘要 iii Abstract iv Table of Contents v List of Figures vii List of Tables viii Chapter 1 Introduction 1 1.1 Motivation 1 1.2 Proposed Technique 3 1.3 Contributions 5 1.4 Organization 6 Chapter 2 Past Research 7 2.1 Background of Power Prediction 7 2.2 Background of Thermal Prediction 10 2.3 Convolutional Neural Network and U-Net 13 2.4 Ansys Fast Transient Thermal Solver 15 Chapter 3 Proposed Techniques 18 3.1 Overall Flow 18 3.2 Pattern Selection 20 3.3 FF Maps Features Creation 22 3.4 EPC Map Feature Creation 25 3.5 Machine Learning 28 3.6 Multiple Models 30 3.7 Power Map Time-Averaging 33 Chapter 4 Experimental Results 35 4.1 Environmental Setup & Evaluation Metric 35 4.2 Power Prediction: Overall Results 37 4.3 Power Prediction: Features and Multiple Models 41 4.4 Thermal Prediction (multi-core) 44 4.5 Runtime Comparison 48 4.6 Disk Storage Comparison 49 Chapter 5 Discussion and Future Work 51 5.1 Machine Learning Model Candidates 51 5.2 Observations of Power Prediction (Multiple Models) 52 5.3 Future Work 54 Chapter 6 Conclusion 57 References 58 | - |
dc.language.iso | en | - |
dc.title | 針對自動產生之測試圖樣的高速且低儲存空間之功率與溫度預測 | zh_TW |
dc.title | High-Speed, Low-Storage Power and Thermal Predictions for ATPG Test Patterns | en |
dc.type | Thesis | - |
dc.date.schoolyear | 111-2 | - |
dc.description.degree | 碩士 | - |
dc.contributor.oralexamcommittee | 張鴻嘉;江蕙如 | zh_TW |
dc.contributor.oralexamcommittee | Norman Chang;Iris Hui-Ru Jiang | en |
dc.subject.keyword | 自動測試圖樣,機器學習,功率預測,溫度預測, | zh_TW |
dc.subject.keyword | ATPG test patterns,machine learning,power prediction,thermal prediction, | en |
dc.relation.page | 62 | - |
dc.identifier.doi | 10.6342/NTU202301935 | - |
dc.rights.note | 未授權 | - |
dc.date.accepted | 2023-08-09 | - |
dc.contributor.author-college | 電機資訊學院 | - |
dc.contributor.author-dept | 電子工程學研究所 | - |
顯示於系所單位: | 電子工程學研究所 |
文件中的檔案:
檔案 | 大小 | 格式 | |
---|---|---|---|
ntu-111-2.pdf 目前未授權公開取用 | 2.87 MB | Adobe PDF |
系統中的文件,除了特別指名其著作權條款之外,均受到著作權保護,並且保留所有的權利。