Welcome to the MICS 6001C - Custom Computing with FPGAs at HKUST(GZ), 2025 Fall

Course Information

Course Description

Being able to customize hardware architecture to application’s exact needs, FPGA-based accelerators deliver better efficiency than general architectures such as CPUs. This course explores the latest advances in FPGA-based accelerators for computation-intensive applications. It covers FPGA architecture fundamentals, underlying structures, and key technologies for high-performance programming. Using C/C++ via HLS tools, the course aims to agile FPGA development. It showcases recent FPGA acceleration achievements across various domains, including DNNs. Learners will engage with multiple design examples, starting with basic designs and offering opportunities for further exploration. Optionally, this course will include accelerator/algorithm co-design, being an extremely important and promising research topic.

Course Schedule

Week Date (Friday) Topics Paper Reading Recommended Readings
Week 1 Sept 4 1. Course Introduction
  1. Domain Specific Architectures | | Domain-specific hardware accelerators. Communications of the ACM 2020. | | Week 2 | Sept 11 | 1. FPGA Architecture
  2. Hardware Programming Languages | | Three Ages of FPGAs: A Retrospective on the First Thirty Years of FPGA Technology, Proceedings of the IEEE, 2015 | | Week 3 | Sept 18 | 1. HLS Overview
  3. Vitis HLS Tutorial (Bring your laptop) | | FPGA HLS today: successes, challenges, and opportunities. TRETS, 2022. | | Week 4 | Sept 25 | Project Kickoff Presentation (Topic; Preliminary results; Plan) | | TAPA: a scalable task-parallel dataflow programming framework for modern FPGAs with co-optimization of HLS and physical design. TRETS, 2023. | | Week 5 | Oct 2 | National Day Holiday (No Class) | | | | Week 6 | Oct 9 | 1. Loops and Optimizations in HLS
  4. Memory and Streaming in HLS | 2 pres. | Allo: A Programming Model for Composable Accelerator Design. PLDI, 2024 | | Week 7 | Oct 16 | 1. HLS Workflow | 2 pre. | A survey of quantization methods for efficient neural network inference. Low-Power Computer Vision. | | Week 8 | Oct 23 | 1. Neural Networks Fundamentals
  5. Data Quantization | 2 pres. | Tpu v4: An optically reconfigurable supercomputer for machine learning with hardware support for embeddings. ISCA, 2023 | | Week 9 | Oct 30 | 1. Convolution Operations in Hardware
  6. Systolic Arrays and their Applications | 2 pres. | Graphit: A high-performance graph dsl. OOPSLA 2018. | | Week 10 | Nov 6 | Project Midterm Presentation (Progress; Findings; Problems; Plan) | | | | Week 11 | Nov 13 | 1. Advanced DSP Techniques
  7. Domain Specific Languages | 2 pres. | ReGraph: Scaling graph processing on HBM-enabled FPGAs with heterogeneous pipelines. MICRO, 2022. | | Week 12 | Nov 20 | 1. Hardware-Accelerated Graph Analytics | 2 pres. | Polardb serverless: A cloud native database for disaggregated data centers." SIGMOD, 2021. | | Week 13 | Nov 27 | 1. Database Acceleration
  8. FPGAs In Cloud Computing | 2 pres. | | | Week 14 | Dec 4 | Final Project Presentation | | |

Group Project Topics

Note: You can propose your own projects

Collecting Project and Paper Presentation Information

  1. Please fill your project information in shared Google Sheets below.
  2. As a gentle reminder, it's advisable to start your project as early as possible.
  3. Link:https://docs.google.com/spreadsheets/d/1_O_gCtVbqMIl4bCt0_IR8nSISDKpqyRgJ1BWgzGIeho/edit?usp=sharing

[https://docs.google.com/spreadsheets/d/1_O_gCtVbqMIl4bCt0_IR8nSISDKpqyRgJ1BWgzGIeho/edit?usp=sharing](https://docs.google.com/spreadsheets/d/1_O_gCtVbqMIl4bCt0_IR8nSISDKpqyRgJ1BWgzGIeho/preview?usp=sharing)

References to Learn HLS-based FPGA Programming