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研究生(外文):Wen-Chun Chao
論文名稱(外文):System-level Exploration for Positive Design Iteration
指導教授(外文):Chingwei YehTay-Jyi Lin
口試委員(外文):Chingwei YehTay-Jyi LinJinn-Shyan WangChun-Ming Huang
外文關鍵詞:neural networkdysarthria voice conversionquantizationembedded systemclustering
  • 被引用被引用:0
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Nowadays, neural networks are widely used in various fields. However, a large number of weights increase the requirement for memory, which needs to be stored in external memory when used in embedded systems. When each neuron calculating, they need to use weight in every computation, which means that the external memory needs to be accessed multiple times during the operation, it really increases energy consumption. The huge hardware cost and the power consumption of accessing external memory make the cost in chip design become significantly high. Therefore, this research uses 40nm dysarthria voice conversion SoC as a research platform, focus on architecture and performance analysis, find out the bottleneck of the system, and re-scheduling reduce 38% of conversion time. Without re-training neural network, I propose that per layer or per neuron K-means clustering can compress the parameters, and the quality loss is significantly improved compared with per network K-means. Considering the above-mentioned clustering method and transmission bandwidth and other conditions, the best design solution of DVC SoC is evaluated.
致謝 ii
Abstract iv
目錄 vi
圖目錄 ix
表目錄 xi
第一章 緒論 1
1.1. 研究動機 1
1.2. 研究貢獻 2
1.3. 論文架構 2
第二章 基於深層神經網路的構音異常語音轉換系統晶片 4
2.1. 構音異常語音轉換 4
2.2. 構音異常語音轉換演算法 6
2.3. 構音異常轉換系統晶片 10
2.4. 排程 16
2.5. 後續的優化課題 16
第三章 以DMA實現較佳排程 20
3.1. 加入DMA對DVC SoC排程的影響 21
3.2. 以frame為單位之DVC SoC排程 22
3.3. 以inference為單位之DVC SoC排程 25
3.4. 排程後續課題 31
第四章 以MI搭配權重量化實現高效率的外部記憶體存取 33
4.1. DNN計算與權重讀取 34
4.2. Flash讀取時間推導 35
4.3.可行的緩解方法 40
4.4.本文方法 44
4.5. K-means介紹 45
4.6. K-means在NN網路的應用 47
4.7. K-means最佳方案探討 50
第五章 優化結果 54
第六章 結論與未來展望 56
參考文獻 58

[1].S. Han, H. Mao, and W. J. Dally. (2016). Deep compression: Compressing
deep neural networks with pruning, trained quantization and Huffman coding [Online].
Available: https://arxiv.org/pdf/1510.00149.pdf
[2].K. Kobayashi and T. Toda, “Sprocket: open-source voice conversion software,” 2018
[3].R. L. MacDonald, et al., “Disordered speech data collection: lessons learned at 1 million utterances from project Euphonia,” in Proc. INTERSPEECH, 2021
[4].J. R. Green, et al., “Automatic speech recognition of disordered speech: personalized models outperforming human listeners on short phrases,” in Proc. INTERSPEECH, 2021
[5].T. -J. Lin et al., "A 40nm CMOS SoC for Real-Time Dysarthric Voice Conversion of Stroke Patients," in Proc. ASP-DAC, 2022, pp. 7-8.
[6].Y. H. Lai, et al., “A deep-learning-based voice conversion system for dysarthria speakers,” in Proc. ASHA, 2018
[9].A. Zermini, et al., “Binaural and log-power spectra features with deep neural networks for speech-noise separation,” in Proc. MMSP, 2017
[10].M. Huang, “Development of taiwan mandarin hearing in noise test,” Department of speech language pathology and audiology, National Taipei University of Nursing and Health Science, 2005.
[11].D. Arthur, S. Vassilvitskii, “k-means++: The advantages of careful seeding," in
Proc. Symp. Discrete Algorithms, 2007.
[12].C. H. Taal, R. C. Hendriks, R. Heusdens, and J. Jensen, “An algorithm for intelligibility prediction of time frequency weighted noisy speech,” in Proc. IEEE Trans. Audio, Speech, Lang, 2011.
[13]. MX25U1635E DATASHEET [Online].
Available: https://datasheetspdf.com/pdffile/792586/MACRONIX/MX25U1635E/1
[14].S. Seo and J. Kim, "Hybrid Approach for Efficient Quantization of Weights in Convolutional Neural Networks," in Proc. BigComp, 2018, pp. 638-641.
[15].Lei, Wang et al. “Compressing Deep Convolutional Networks Using K-means Based on Weights Distribution.” IIP'17 (2017).
[16].Y. Gong, L. Liu, M. Yang and L. Bourdev, “Compressing deep convolutional
networks using vector quantization,” in Proc. ICLR, 2015.
[17].E. Dupuis, D. Novo, I. O’Connor and A. Bosio, "Sensitivity Analysis and Compression Opportunities in DNNs Using Weight Sharing," in Proc. DDECS, 2020, pp. 1-6.
[18].S. Han, J. Pool, J. Tran, and W. J. Dally. (2015). Learning both weights and connections for efficient neural networks [Online].
Available: https://arxiv.org/pdf/1506.02626.pdf
[20].Van Leeuwen, Jan, “On the construction of huffman trees”. in Proc. ICALP, 1976, pp. 382–410.
[21].Denton, Emily, Zaremba, Wojciech, Bruna, Joan, LeCun, Yann, and Fergus, Rob. (2014). Exploiting linearstructure within convolutional networks for efficient evaluation[Online].
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