Things to learn for new students in Lab615
Basic Engineering Skills You Have to Master
Software Programming
Learn the following two programing langrages
- C/C++ (C99/C++11)
- Python (v3.0)
Do the following projects to practice and learn good habits when programming in C
Here's another good repo that has lots of good projects for you to practice.
- Project-Based-Tutorials-in-C (try 'Emulator 101', 'hash table', 'How to Write a Video Player in Less Than 1000 Lines')
Learn the following Good Coding Styles and use them in your research projects:
- C语言的语法风格与代码书写规范指南 （简单）
- NASA C coding style, NASA, 1994 （推荐）
- Recommended C Style and Coding Standards, UC Berkeley, 1997 （简单）
- Guidelines for the use of the C language in critical systems, MISRA, 2018 （高级）
- A list of C and C++ Style Guides
- Embedded C programming
Some other good projects based on C/C++
Deep Learning Frameworks
- 动手学深度学习PyTorch版（入门推荐）
- Pytorch中文教程
- Nvidia官方training、inference例子、参考代码
Excellent Deeplearning Project Using Pytorch
There are many good examples that we have collected from github, and students can learn how to write "good" pytorch codes by reading and modifying the codes from these example project. A more complete list is here.
How to setup the GPU and Pytorch environment
Hardware Basic Knowledge
Read the following two books to learn basic concepts for digital circuit design and computer architecture.
Digital Circuit/Logic Design
Important concepts to understand include combinatonal logic and sequential logic (组合逻辑和时序逻辑), register寄存器(Flip-Flop circuit), FSM状态机, counter计数器, decoder/encoder编码器和译码器, FIFO, RAM, etc. Read the following two books:
- ''数字逻辑设计与计算机组成, 戴志涛等，机械工业出版社''
- "Digital Logic"
Computer Architecture
Important concepts to understand include pipeline, memory hierarchy, roofline model, Amdahl's law, ILP (instruction level parallelism), TLP (task level parallelism), DLP (data level parallelism), SIMD/VLIW processor, etc. Read the following two books:
- ''Computer Organization and Design The Hardware Software Interface'', ARM Edition, 2017（零基础学习）
- ''Computer Architecture A Quantitative Approach'', 6th Edition, 2019 （进阶学习）
More reading:
- Loop-carried dependency: 1, 2
- Roofline Model Basic: ./doc/Roofline Model.pdf
- 并行处理的几种常见方式（推荐）
FPGA Design
After you have basic knowledges on digital circuit and computer architectures, you could learn FPGA design or heterogenours computing (using FPGA as accelerators). We recommand using HLS (High-level Synthesis)-based schemes (C/C++-based HLS or OpenCL) rather than RTL-level programming (i.e., Verilog and VHDL) to design application specific circuit on FPGAs. Read the following book to learn OpenCL programing (GPU/FPGA):
- ''OpenCL Programming Guide'', Aaftab Munshi, et.al., 2012 [also known as The Green Book]
- ''FPGA异构计算——基于OpenCL的开发方法'', 黄乐天 等, 2015
Also, refers to Intel/Xilinx's OpenCL user guide to learn specific techniques that will be used in the project.
Finally, learn our opensource project PipeCNN. Run the examples, such as caffenet, vgg-16, resnet, YOLO on the DE10-nano and DE5-net platforms. Learn how to configure, compile, debug the source codes and profile the performance of the accelerator.
Zhang DeZheng has wrote a good study note on PipeCNN, please read it here.
Information on the FPGA boards used in our lab here.
A list of GOOD FPGA accelerator design can be found here
GPU Design
Learn TensorRT and CUDA programing. Try examples on our TX2/TK1 platforms.
Research Related Topics
First, students should read the following artichles to learn how to write research papers.
- ''How to write a great research paper'', Deep Learning Indaba, Stellenbosch, 2018
- ''How to Publish a Research Paper'', wikiHow, 2019
- ''How to Write a Good Scientific Paper'', Chris A. Mack, SPIE, 2018
- ''How to Write a Good Paper in Computer Science and How Will It Be Measured by ISI Web of Knowledge'', R?zvan Andonie, et.al., 2010
Secondly, read the following selected papers in each research topics, which are really good examples in the related fields.
Tutorials for Hardware Architectures for DNN
Students who are working on hardware designs for deep neural networks should read the following tutorials.
- Hardware Architectures for Deep Neural Networks, MICRO Tutorial 2016. （推荐）
FPGA Accelerator Design
- Optimizing FPGA-based Accelerator Design for Deep Convolutional Neural Networks, FPGA 2015.
- Throughput-Optimized OpenCL-based FPGA Accelerator for Large-Scale Convolutional Neural Networks, FPGA 2016.
- An OpenCL Deep Learning Accelerator on Arria 10, FPGA 2017.
- Improving the Performance of OpenCL-based FPGA Accelerator for Convolutional Neural Network, FPGA 2017.
- A Framework for Generating High Throughput CNN Implementations on FPGAs, FPGA 2018.
- An Efficient Hardware Accelerator for Sparse Convolutional Neural Networks on FPGAs, FCCM 2019.
The following survery papers are also worth reading.
- A Survey of FPGA Based Neural Network Accelerator, ACM TRETS 2017.
- Deep Neural Network Approximation for Custom Hardware: Where We’ve Been, Where We’re Going, ACM Computing Surveys 2019.
Our own research papers on FPGA accelerators:
- PipeCNN: An OpenCL-Based Open-Source FPGA Accelerator for Convolution Neural Networks, FPT 2017
- ABM-SpConv: A Novel Approach to FPGA-Based Acceleration of Convolutional Neural Network Inference, DAC 2019
A more complete paper list is here.
Sparse Convolution Design
A more complete list is here.
Neural network optimization (quantization, pruning, et.al.)
Neural Network Quantization
- Ristretto: A Framework for Empirical Study of Resource-Efficient Inference in Convolutional Neural Networks, IEEE T-NNLS 2018.
- 8-bit Inference with TensorRT, Nvidia 2017.
- Quantizing deep convolutional networks for efficient inference: A whitepaper, Google, 2018.
A more complete list is here.
Network Pruning and Compression
A more complete list is here.
Neural Architecture Search (NAS)
A more complete list is here.
Object Detection
A more complete list is here.