HLS Hackathon

Wednesday, Jul 02nd-8:00 AM PDT

Accelerating Inferencing Using HLS Hackathon

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SIEMENS HLS Academy

Accelerating Inferencing Using HLS Hackathon

Energy efficiency is essential for edge devices, especially those powered by batteries or harvested energy, making low-power AI and machine learning inference a real challenge. In this hackathon focused on accelerating inferencing using HLS, your mission is to build a high-efficiency hardware accelerator that delivers accurate predictions with minimal energy per inference.

Russell Klein Cameron Villone

  1. MNIST Hackathon

    1. MNIST Overview

      Overview

      Energy can be critical in edge devices. Systems that are battery powered or rely on harvested energy need to be as efficient as possible. Which can make deploying inferencing on these systems challenging. Inferencing is notoriously power hungry. In this hackathon you’ll build an efficient inferencing accelerator. The winner will be the team that can build the most efficient inferencing system, that is the one that delivers predictions with the smallest amount of energy per inference. Your implementation will need to meet strict performance, accuracy, and area requirements, too.

      Oh, and there’s a time limit. You’ll have 30 days to complete your masterpiece of efficient engineering. You might want to put in for vacation time now. And stock up on your favorite caffeinated beverage.

      *Pro tip: #JoltCola, the original energy drink, is making a comeback, with all the sugar and now even more caffeine!

  2. Leaderboard

    1. Leaderboard

  3. The Algorithm

    1. The Algorithm

      We’re going to be using the MNIST handwritten character recognition algorithm. Sure, it’s old and it’s small, but it is the one Yann LeCun got started with (and he’s now the chief AI guy at Meta).

      We picked this because it's small enough to retrain in a few minutes, it's practical to run in logic simulation, and it can be characterized quickly. Bottom line, you’re going to be able to get through more design iterations faster. Which you’re going to need to zero in on the optimal architecture.

  4. The Starting Line

    1. The Starting Line

      You’ll be given a virtual machine equipped with all the tools and IP you’ll need to build and characterize an ASIC implementation of your inferencing accelerator, courtesy of Siemens EDA. You’ll start with RocketCore RISC-V design, and a bare metal application that runs the MNIST algorithm. Your job is to make the inference run faster than any software implementation could possibly go, all while your design sips tiny amounts of energy to get the job done. It’s a unique opportunity to flex your creativity, only if you're up for the challenge.

  5. Objectives Title

    Objectives

  6. Accuracy

    1. Objective 1

      First and foremost is accuracy. Your implementation will need to be able to correctly recognize 95% of the images from the MNIST database. Any less accurate, sorry, you won’t make the cut.

    2. Accuracy Image

  7. Performance

    1. Objective 2

      Second, is performance. No slow-pokes allowed. Your inference must complete in less than 20 milliseconds.

    2. Performance Image

  8. Area

    1. Objective 3

      Third, there’s only so much space on the die. Whatever you build, it needs to fit into 1,000,000 square microns, based on the AISC library provided. This includes the area used by the accelerators and the memory needed to hold the weights and any intermediate values.

    2. Area Image

  9. Energy consumption

    1. Objective 4

      Finally, if your design is accurate enough, fast enough, and small enough, then our panel of esteemed judges will evaluate it. No style points here, though. There’s just one thing we’re going to look at, and that is energy per inference. Remember Ohm’s law? Energy is power times time. We will measure the time it takes the inference to complete multiplied by the sum of the dynamic power and the leakage power, averaged over 10 inferences.


    2. Energy Consumption Image

  10. Objectives Conclusion

    Will your strategy be to go as fast as possible? Or do you want your power to be as low as possible, but mosey through the calculations? Or perhaps the middle of the road, kinda fast and kinda low power? Check the leaderboard weekly to see how your design compares.

  11. Hackathon Getting Started

    Do you have what it takes?

    Winning this hackathon will require a true renaissance engineer. The skills needed range from Python, machine learning, and system design to low level power optimization in RTL. C and C++ programming prowess is essential, as are hardware design techniques and a basic understanding of SystemVerilog.

    Rules & Regulations

    Contest rules

    Getting Started

    Steps to get started:

    1. Get access to ODT:
      1. Use the 30-Day Free Catapult Training Promo Code at checkout: ExploreVEP__30 to make it free to you for 30 days!
      2. Go to Catapult's library and click on "Buy"
    2. Once you have access navigate to the learning path High-Level Synthesis 2025 Hackathon.
      1. We recommend that you use the HTML5 version to launch the VLab instead of using Citrix.
    3. Complete the learning path and Vlabs
    4. Submit your results by October 31, 2025. You must be logged in with a full-access account or register for one.

  12. Energy & Efficiency Image

    1. Energy & Efficiency Image

  13. Winning Criteria

    The Winning Criteria

    The winning design will be the one that meets the accuracy, performance, and area criteria, and consumes the least average energy per inference. You’ll be using PowerPro from Siemens EDA to measure the power of your combined hardware and software system. May the best design win!

  14. Prizes

    Prizes, Trophies, and Bragging Rights

    Of course, the winners will have bragging rights. Post it up on LinkedIn that you won the High-Level Synthesis low-energy inferencing hackathon! Your achievements will reach legendary status🔝.

    But wait, there’s more. Winners of a hardware design contest deserve hardware. As one of the top 3 winners, you will get a tasteful trophy commemorating your victory. Suitable for a place of distinction in your office or cubicle, where your co-workers and managers can marvel at your awesomeness😎.

    There's even more...

    🥇The first-place winner will receive Elegoo’s Neptune 3D printer, and an opportunity to speak at the Edge AI foundation’s Fall Taipei event (physically or virtually) to tell the world in your own words how you reached such amazing heights of success. Literally, and we literally mean this “literally,” fame and fortune.

    🥈The second-place winner will receive a Pynq FPGA development board from Digilent, to hone their skills for next year’s competition. The Pynq board combines an ARM processor with Python and AI capabilities with FPGA fabric from AMD.

    🥉The third-place winner will receive a pair of Bose QuietComfort Earbuds. Bose QuietComfort Earbuds offer iconic audio, renowned noise cancellation, and a relentless, long-lasting battery, all in a compact, durable package.

    All participants who complete the hackathon with a valid submission will receive a badge suitable for promoting their engineering genius on LinkedIn. (Psst 🤫, recruiters pick up on this kind of thing😉)

    💡Submission Link will only show for users with full-access accounts that are logged in. You must be logged in with your HLS Academy full-access account or register for one to see the submission link for your design. Please ensure you've confirmed your consent via the email we sent. Approval may take 12 hours.