Beethoven Tutorial at ISCA 2025
Tutorial for Beethoven at ISCA 2025
🚀 Overview
Welcome to the Beethoven Tutorial, your gateway to mastering open-source accelerator design! Beethoven lets you quickly prototype powerful accelerated systems based on the “separation of concerns” philosophy. Focus purely on crafting your accelerator Core, while Beethoven handles the heavy lifting by auto-generating optimized system components, making high-performance system creation both enjoyable and accessible!
‼️ [IMPORTANT] Tutorial Pre-work Form
For those who would like to attend the tutorial, please fill this form before attending.
Link: Pre-work Form
Pre-work Instructions: Slides
📅 Tutorial Details
- 🕒 Duration: From 8 AM to 1:30 PM
- 👥 Expected Audience: 10-30 participants
- 🌟 Who Should Attend? Researchers, industry professionals, and anyone interested in accelerator technologies. No prior FPGA or Beethoven knowledge required!
🎤 Organizers
- Lisa Wu Wills (Duke University, lisa@cs.duke.edu)
- Chris Kjellqvist (Duke University, christopher.kjellqvist@duke.edu)
- Mason Ma (Duke University, jiaao.ma@duke.edu)
- Mansi Choudhary (Duke University, mansi.choudhary@duke.edu)
- Entropy Xu (Hong Kong University of Science and Technology, eeentropy@ust.hk)
🗓️ Tutorial Schedule
| 🕒 Time | 📖 Topic | 🎙️ Speaker(s) |
|---|---|---|
| 8:00–8:30 | 🎵 Intro to Beethoven & Hardware Landscape | Lisa Wu Wills |
| 8:30–9:00 | 🧩 Beethoven Abstractions & Code Structure | Chris Kjellqvist |
| 9:00–9:30 | ⚙️ Example Accelerator Core | Mansi Choudhary |
| 9:30–10:00 | ⚙️ Hands-on: Write Your Own Simple Accelerator Core (Chisel or Verilog) | Everyone |
| 10:00–10:30 | ☕ Coffee Break | — |
| 10:30–11:00 | 🧪 Beethoven Integration + Testing | Chris Kjellqvist |
| 11:00–11:30 | ⚙️ Example Accelerator Beethoven Integration | Mason Ma |
| 11:30–12:00 | 🧪 Hands-on: Integrate Your Own Simple Accelerated System + Testing | Everyone |
| 12:00 | 🍱 Pick Up Box Lunch | — |
| 12:00–12:15 | 🧪 Hands-on: Compile/Build FPGA Tarball | Everyone |
| 12:15–1:00 | ⏳ Wait for FPGA Tarball to Build | — |
| 1:00–1:15 | 📊 Example Accelerated System Evaluation | Chris Kjellqvist |
| 1:15–1:30 | 📊 Hands-on: Deploy and Evaluate Your Own Accelerated System | Everyone |
☁️ Infrastructure
Participants will get hands-on experience with AWS EC2 F2 cloud instances generously funded by Duke University. Deploy and experiment with your Beethoven-generated systems in the cloud seamlessly!
👩🏫 Speaker Bios
Lisa Wu Wills
Assistant Professor of Computer Science and ECE at Duke University. Prior to Duke, she was a postdoctoral researcher at UC Berkeley and a research scientist at Intel Labs. Her research interests include computer architecture and microarchitecture, hardware acceleration, hardware-software co-design, emerging applications in big data, healthcare, and artificial intelligence. Wills has a PhD in computer science from Columbia University. Her research is recognized via various awards such as an NSF CAREER Award, a VMware Early Career Faculty Grant, IEEE Micro Top Picks (x3) and Honorable Mentions (x2), and best paper awards from MICRO and ISPASS.
Chris Kjellqvist
Fifth-year PhD student in Computer Science at Duke University. He has a BS from the University of Rochester. His research leverages modern hardware description languages’ flexible, generative ability and programming abstraction to provide scalable and reusable SoC infrastructure for hardware accelerator development. This has exposed him to nearly every corner of accelerator design, from high-level architectural analysis to accelerator pipeline design using RTL languages, physical layout and implementation on FPGAs and an ASIC test chip, and software and operating system integration. This experience has given him a basic insight into just how complex real-world systems must be to operate correctly and efficiently. He is the lead architect of the Beethoven project.
Mason Ma
Fourth-year PhD student in Computer Science at Duke University. His research focuses on efficient software and hardware design for privacy-preserving computing, with a particular emphasis on advancing fully homomorphic encryption (FHE) through optimizations in arithmetic, compilers, and hardware accelerators. He has developed hardware architectures and ML compilers that optimize FHE computations, achieving significant speedups and efficiency improvements for privacy-preserving natural language processing and data analysis tasks. In his doctoral work, he has extensively leveraged GPU architectures to accelerate complex computations. Notably, he developed high-performance CUDA execution engines for FHE applications, achieving up to a 120 times speedup over prior GPU executors. This experience has given him deep insights into GPU programming and optimization, as well as a strong understanding of how to map complex cryptographic algorithms efficiently onto parallel architectures. His work has won an ISPASS Best Paper.
Mansi Choudhary
Third-year PhD student in ECE at Duke University. Her primary area of research is computer architecture, with an emphasis on workload analysis, performance modeling, and hardware acceleration through architectural and microarchitectural enhancements for domain-specific applications, including AI. Her work aims to improve performance and power efficiency in these systems.
Entropy Xu
Postdoctoral researcher at the Hong Kong University of Science and Technology. Xu has a PhD from Duke University. His dissertation was nominated by Duke for the ACM Dissertation Award. His research focused on tackling two major challenges with innovative solutions: leveraging the performance and power efficiency of hardware specialization to architect innovative accelerators for artificial intelligence (computer architecture for AI), and leveraging various deep-learning techniques to significantly expedite the development of hardware accelerators (AI for computer architecture). His work has been awarded an ISPASS Best Paper Award and selected for an IEEE Micro Top Pick Honorable Mention.