簡易檢索 / 詳目顯示

研究生: 林亭汝
Ting-Ju Lin
論文名稱: 邊緣應用之門控循環單元網路之積體電路設計與驗證
Integrated Circuit Design And Verification of A Gated Recurrent Unit Network for Edge Applications
指導教授: 彭盛裕
Sheng-Yu Peng
口試委員: 吳安宇
An-Yeu Wu
陳新
Hsin Chen
謝易錚
Yi-Zeng Hsieh
學位類別: 碩士
Master
系所名稱: 電資學院 - 電機工程系
Department of Electrical Engineering
論文出版年: 2023
畢業學年度: 111
語文別: 英文
論文頁數: 89
中文關鍵詞: 機器學習硬體類比門控循環單元神經網路邊緣運算硬體友善演算法軟硬體共同設計
外文關鍵詞: machine learning (ML) hardware, analog GRU, edge computing, hardware-friendly algorithm, software and hardware co-design
相關次數: 點閱:264下載:0
分享至:
查詢本校圖書館目錄 查詢臺灣博碩士論文知識加值系統 勘誤回報

  • Abstract in Chinese . . . . . . . . . . . . . . . . . . . . vi Abstract in English . . . . . . . . . . . . . . . . . . . . vii Acknowledgements . . . . . . . . .. . . . . . . . . . . . . viii Contents . . . . . . . . . . . . . .. . . . . . . . . . . . ix List of Figures . . . . . . . . . . . . . . . . . . . . . . xii List of Tables . . . . . . . . . . .. . . . . . . . . . . . xvii 1 Introduction . . . . . . . . . . .. . . . . . . . . . . . 1 1.1 Motivation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1 1.2 Thesis Organization . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7 2 Background Knowledge . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.1 Edge Applications . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 8 2.2 Gated Recurrent Unit Neural Network . . . . . . . . . . . . . . . . . . . 10 3 Analog Gated Recurrent Unit Neural Network Circuit and System . . . . . . . 14 3.1 System-Level Design With Proposed Analog Gated Recurrent Unit Neural Network . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 14 3.1.1 Hardware-Friendly Software Model . . . . . . . . . . . . . . . . 14 3.1.2 Training Procedure . . . . . . . . . . . . . . . . . . . . . . . . . 18 3.1.3 Applied in Classification Problem . . . . . . . . . . . . . . . . . 23 3.2 Proposed Analog Gated Recurrent Unit Neural Network Architecture . . 25 3.2.1 Architecture . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 25 3.2.2 Feature Extraction . . . . . . . . . . . . . . . . . . . . . . . . . 26 3.2.3 SRAM-Based Reconfigurable Cognitive Computation Matrix (RCCM) 29 3.2.4 Activation Function . . . . . . . . . . . . . . . . . . . . . . . . 33 3.2.5 Memory State . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37 3.2.6 Common Gate Normalization . . . . . . . . . . . . . . . . . . . 41 3.3 Co-Design Between Software and Hardware . . . . . . . . . . . . . . . . 43 3.3.1 Adjusted Analog Gated Recurrent Unit Neural Network Model . . 44 3.3.2 Simulation Results Applied to Spike Detection . . . . . . . . . . 49 3.4 Measurement Result and Comparison . . . . . . . . . . . . . . . . . . . 54 3.4.1 Feature Extraction Measurement Result . . . . . . . . . . . . . . 55 3.4.2 Activation Functions Measurement Result . . . . . . . . . . . . . 56 3.4.3 Memory State Measurement Result . . . . . . . . . . . . . . . . 58 3.4.4 On-Off Keying Measurement Result . . . . . . . . . . . . . . . . 61 3.4.5 Voice Activity Detection Measurement Result . . . . . . . . . . . 64 3.5 Comparison Table . . . . . . . . . . . . . . . . . . . . . . . . . . . 66 4 Conclusion and Contribution . . . . . . . . . . . . . . . . . . . . . . 68 4.1 Conclusion . . . . . . . . . . . . . . . . . . . . .. . . . . . . . . 68 4.2 Contribution . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 69 5 Future Works . . . . . . . . . . . . . . . . . . . . . . . . . . . . 70 References . . . . . . . . . . . .. . . . . . . . . . . . . . . 71

    [1] J. S. P. Giraldo and M. Verhelst, “Laika: A 5uw programmable lstm accelerator for always-on keyword spotting in 65nm cmos,” in ESSCIRC 2018-IEEE 44th European Solid State Circuits Conference (ESSCIRC), pp. 166–169, IEEE, 2018.
    [2] J. S. P. Giraldo, S. Lauwereins, K. Badami, and M. Verhelst, “Vocell: A 65-nm speech-triggered wake-up soc for 10- μ w keyword spotting and speaker verification,” IEEE Journal of Solid-State Circuits, vol. 55, no. 4, pp. 868–878, 2020.
    [3] Q. Li, C. Liu, P. Dong, Y. Zhang, T. Li, S. Lin, M. Yang, F. Qiao, Y. Wang, L. Luo, and H. Yang, “Ns-fdn: Near-sensor processing architecture of feature-configurable distributed network for beyond-real-time always-on keyword spotting,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 68, no. 5, pp. 1892–1905, 2021.
    [4] K. Odame, M. Nyamukuru, M. Shahghasemi, S. Bi, and D. Kotz, “Analog gated recurrent unit neural network for detecting chewing events,” IEEE Transactions on Biomedical Circuits and Systems, vol. 16, no. 6, pp. 1106–1115, 2022.
    [5] J. De Roose, H. Xin, A. Hallawa, G. Ascheid, P. J. Harpe, and M. Verhelst, “Flexible, self-adaptive sense-and-compress soc for sub-microwatt always-on sensory recording,” IEEE Solid-State Circuits Letters, vol. 3, pp. 362–365, 2020.
    [6] M. Croce, B. Friend, F. Nesta, L. Crespi, P. Malcovati, and A. Baschirotto, “A 760- nw, 180-nm cmos fully analog voice activity detection system for domestic environment,” IEEE Journal of Solid-State Circuits, vol. 56, no. 3, pp. 778–787, 2020.
    [7] E. Shi, X. Tang, and K. P. Pun, “A 270 nw switched-capacitor acoustic feature extractor for always-on voice activity detection,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 68, no. 3, pp. 1045–1054, 2020.
    [8] Y. Zhao, Z. Shang, and Y. Lian, “A 13.34 μw event-driven patient-specific ann cardiac arrhythmia classifier for wearable ecg sensors,” IEEE Transactions on Biomedical Circuits and Systems, vol. 14, no. 2, pp. 186–197, 2020.
    [9] D. Bankman, L. Yang, B. Moons, M. Verhelst, and B. Murmann, “An always-on 3.8 μ j/86chip in 28-nm cmos,” IEEE Journal of Solid-State Circuits, vol. 54, no. 1, pp. 158–172, 2019.
    [10] J.-H. Kim, C. Kim, K. Kim, and H.-J. Yoo, “An ultra-low-power analog-digital hybrid cnn face recognition processor integrated with a cis for always-on mobile devices,” in 2019 IEEE International Symposium on Circuits and Systems (ISCAS), pp. 1–5, IEEE, 2019.
    [11] W. Shan, M. Yang, T. Wang, Y. Lu, H. Cai, L. Zhu, J. Xu, C. Wu, L. Shi, and J. Yang, “A 510-nw wake-up keyword-spotting chip using serial-fft-based mfcc and binarized depthwise separable cnn in 28-nm cmos,” IEEE Journal of Solid-State Circuits, vol. 56, no. 1, pp. 151–164, 2020.
    [12] K. Cho, B. Van Merriënboer, C. Gulcehre, D. Bahdanau, F. Bougares, H. Schwenk, and Y. Bengio, “Learning phrase representations using rnn encoder-decoder for statistical machine translation,” arXiv preprint arXiv:1406.1078, 2014.
    [13] R. Guo, Y. Liu, S. Zheng, S.-Y. Wu, P. Ouyang, W.-S. Khwa, X. Chen, J.-J. Chen, X. Li, L. Liu, M.-F. Chang, S. Wei, and S. Yin, “A 5.1pj/neuron 127.3us/inference rnn-based speech recognition processor using 16 computing-in-memory sram macros in 65nm cmos,” in 2019 Symposium on VLSI Circuits, pp. C120–C121, 2019.
    [14] D. Kadetotad, S. Yin, V. Berisha, C. Chakrabarti, and J.-s. Seo, “An 8.93 tops/w lstm recurrent neural network accelerator featuring hierarchical coarse-grain sparsity for on-device speech recognition,” IEEE Journal of Solid-State Circuits, vol. 55, no. 7, pp. 1877–1887, 2020.
    [15] K. Kim, C. Gao, R. Graça, I. Kiselev, H.-J. Yoo, T. Delbruck, and S.-C. Liu, “A 23μw solar-powered keyword-spotting asic with ring-oscillator-based time-domain feature extraction,” in 2022 IEEE International Solid- State Circuits Conference (ISSCC), vol. 65, pp. 1–3, 2022.
    [16] C. Frenkel and G. Indiveri, “Reckon: A 28nm sub-mm2 task-agnostic spiking recurrent neural network processor enabling on-chip learning over second-long timescales,” in 2022 IEEE International Solid- State Circuits Conference (ISSCC), vol. 65, pp. 1–3, 2022.
    [17] G. Paulin, F. Conti, L. Cavigelli, and L. Benini, “Vau da muntanialas: Energyefficient multi-die scalable acceleration of rnn inference,” IEEE Transactions on Circuits and Systems I: Regular Papers, vol. 69, no. 1, pp. 244–257, 2022.
    [18] K. M. H. Badami, S. Lauwereins, W. Meert, and M. Verhelst, “A 90 nm cmos, 6μw power-proportional acoustic sensing frontend for voice activity detection,” IEEE Journal of Solid-State Circuits, vol. 51, no. 1, pp. 291–302, 2016.
    [19] T. Delbruck, “’bump’ circuits for computing similarity and dissimilarity of analog voltages,” in IJCNN-91-Seattle International Joint Conference on Neural Networks, vol. i, pp. 475–479 vol.1, 1991.
    [20] C. Hammerschmied and Q. Huang, “Design and implementation of an untrimmed mosfet-only 10-bit a/d converter with -79-db thd,” IEEE Journal of Solid-State Circuits, vol. 33, no. 8, pp. 1148–1157, 1998.
    [21] B. A. Minch, “Analysis and synthesis of static translinear circuits,” School of Electrical and Computer Engineering, Cornell University, NY, pp. 95–1, 2000.

    無法下載圖示 全文公開日期 2033/07/06 (校內網路)
    全文公開日期 2121/07/06 (校外網路)
    全文公開日期 2121/07/06 (國家圖書館:臺灣博碩士論文系統)
    QR CODE