Syllabus

　

Course Title: Neural Network Acceleration (神經網路加速技術)

Course No. 53026

Semester: Fall, 2020

Time and Classroom: Mon. +13:30-14:05-17:00 (E404)

Credits: 3

Lector: Professor Tsung-Chu Huang (黃宗柱)

Email: tch@cc.ncue.edu.tw

Tel. (04)723-2105 ext. 8384

Website: http://testlab.ncue.edu.tw/tch

Cloud: https://drive.google.com/drive/folders/1EZG40mE2-NDZH9r5QzjHjP5nreGNSJvw?usp=sharing

 

• Schedule and Contents

      Week   1    Introduction to Neural Network; Lab1: Python Programming in Jupyter Online and Colab

      Week   2    Review of Linear Algebra; Lab2: Introduction to Anaconda/Tensorflow/Keras (Xilinx Workshop)

      Week   3    (Leaving for ICCE-TW2020)

      Week   4    Types of Neural Networks; Lab3: Introduction to CNN

      Week   5    Issues of NNs: Overfitting, Dynamic Range Inflation; Lab4: Introduction to RNN

      Week   6    Data Flow Graph/Design; Lab5: Introduction to Pynq Platform and Workshop Sessions 1-2

      Week   7    HW Acceleration: GPU, NPU, VPU, CPU-based Acceleration; Lab6: Xilinx Workshop Sessions 3-4

      Week   8    Approximating Computing: Analogue, LUT, Range LUT, Binary Truncation, AC for Batch Learning; Lab7: RCAM-LUT

      Week   9    Midterm: Report and presentation on NN Acceleration

      Week 10    Residue Number System; Lab8: MAC Array

      Week 11    Matrix-Operation-based Pruning Techniques; Lab9: Pruning

      Week 12    SW Acceleration: Efficient Algorithms, Pruning, Random Dropping; Lab10: Backpropagation

      Week 13    Network Optimization; Lab11: HLS

      Week 14    Model Optimization; Lab12: HLS + HLx

      Week 15    Model Compression; Lab13: Tensor Array

      Week 16    DMA, CUDA, Memory Allocation; Lab14: Approximating

      Week 17    Hybrid I/O and Codesign; Lab15: Configuration Automation

      Week 18    Analogue Neuromorphic Network： RRAM, MRAM, Charge-Capacitor, OTA－based

      Week 19    Demo of Final Project: Applying your technique in Pynq Accelerator

  　

• Textbook and References

  • Textbook: (eBook bought by NCUE and available in Campus)

    • Kevin Gurney. An Introduction to Neural Networks. CRC Press, 1st ed., ISBN 9781857285031, Agu. 5, 1997.

    • R.M. Hritsev. The ANN Book. released under the GNU Public License, 1st Ed. 1998.

    • S. Shanmuganathan and S. Samarasinghe. Artificial Neural Network Modelling. Springer. 2016. (eBook in NCUE)

  • Movies:

    • 3Blue1Brown. Introduction to Artificial Neural Network. https://www.youtube.com/watch?v=aircAruvnKk.

    • PYNQ--AI菜鸟的升级必备(菜鳥強心劑)。https://www.youtube.com/watch?v=LilOaVBYXJo.

    • 周莫凡(莫煩)，科普: 人工神经网络 VS 生物神经网络。https://www.youtube.com/watch?v=lAaCeiqE6CE&list=PLXO45tsB95cKI5AIlf5TxxFPzb-0zeVZ8.

    • CCU MOOCs. "深度學習", https://www.youtube.com/watch?v=wEL5nWBoThw&list=PLQn99bzkJv9wxnnU3nfuFm1MtWDbtps0P.

    • Seeed. PYNQ Z2 FPGA Development board | Hardware Overview.  https://www.youtube.com/watch?v=z3dZb5Y-vvo.

    • FPGAdeveloper. How to setup the PYNQ-Z1. https://www.youtube.com/watch?v=lfw6T9vNM_o.

    • FPGAdeveloper. Xilinx Pynq Workshop Series.  https://www.youtube.com/watch?v=LoLCtSzj9BU&list=PLIs1b8ziMXQZeg06pmqSquK70ydQLA4y5.

    • FPGAdeveloper. How to make a custom PYNQ overlay. https://www.youtube.com/watch?v=Dupyek4NUoI.

    • FPGAdeveloper. How to accelerate a Python function with PYNQ. https://www.youtube.com/watch?v=LoLCtSzj9BU.

    • FPGAdeveloper. Using AXI DMA in Vivado. https://www.youtube.com/watch?v=Yklu68WopBo.

    •  FPGAdeveloper. Creating a custom AXI-Streaming IP in Vivado. https://www.youtube.com/watch?v=R8MSpEU7UKE.

    • FPGAdeveloper. PYNQ Computer Vision Demo: 2D Filter & Dilate. https://www.youtube.com/watch?v=tzQlyEj71Us.

    • Hardware.ai. PYNQ Z2 First Boot and ML acceleration examples. https://www.youtube.com/watch?v=FA3jiIkoN-Q.

    • HR G. Top 5 fastest supercomputers in the world. https://www.youtube.com/watch?v=PoEVBYdUJjk.

    • CNBC. What Is A Supercomputer? https://www.youtube.com/watch?v=utsi6h7IFPs.

  • Links:

    • SD ImageWriter: https://sourceforge.net/projects/win32diskimager

    • PYNQ Image: https://pynq.readthedocs.io/en/v2.2.1/getting_started/pynq_image.html

    • Vivado AXI GPIO basic settings: https://www.youtube.com/watch?v=3SSKzK84AnE (in Chinese)

  •  

  • Reference:

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    [2] K. Jearanaitanakij and O. Pinngern, "An Information Gain Technique for Acceleration of Convergence of Artificial Neural Networks," 2005 5th International Conference on Information Communications & Signal Processing, Bangkok, 2005, pp. 349-352, doi: 10.1109/ICICS.2005.1689065.

    [3] F. Artoni, D. Martelli, V. Monaco and S. Micera, "Principal component analysis can decrease neural networks performance for incipient falls detection: A preliminary study with hands and feet accelerations," 2016 38th Annual International Conference of the IEEE Engineering in Medicine and Biology Society (EMBC), Orlando, FL, 2016, pp. 6194-6197, doi: 10.1109/EMBC.2016.7592143.

    [4] Seung-Joon Lee and Dong-Jo Park, "New accelerated learning algorithm motivated from novel shape of error surfaces for multilayer feedforward neural networks," Proceedings of 1993 International Conference on Neural Networks (IJCNN-93-Nagoya, Japan), Nagoya, Japan, 1993, pp. 553-556 vol.1, doi: 10.1109/IJCNN.1993.713975.

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    [6] L. Liu, J. Luo, X. Deng and S. Li, " C. Xue, S. Cao, R. Jiang and H. Yang, "A Reconfigurable Pipelined Architecture for Convolutional Neural Network Acceleration," 2018 IEEE International Symposium on Circuits and Systems (ISCAS), Florence, 2018, pp. 1-5, doi: 10.1109/ISCAS.2018.8351425.," 2015 10th International Conference on P2P, Parallel, Grid, Cloud and Internet Computing (3PGCIC), Krakow, 2015, pp. 824-827, doi: 10.1109/3PGCIC.2015.103.

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• Links

  • http://pynq.io

  • Pynq Workshop