SIEMENS HLS Academy

Roadside Recognition Using HLS Hackathon

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.
Stay tuned, the hackathon kicks off soon!

Russell Klein Matt Bone

  1. Winners and time limit

    1. Winners and time limit

      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.

  2. Leaderboard

    1. Leaderboard

  3. The Algorithm – roadside recognition

    We’re going to be using an object recognition algorithm. It will recognize ten kinds of objects found on the side of the road. It will process an input of a 20x20 pixel image and try to match it to one of ten categories (really similar to MNIST).

    While it processes a tiny image, 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

    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 recognition 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.

  5. Objectives Title

    Objectives

  6. Objectives image

  7. Objectives Conclusion

    Will your strategy be to go as fast as possible? Or did 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?

  8. Do you have what it takes

    1. 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.

  9. Winning Criteria

    1. Winning Criteria Recap

      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!

  10. 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...

    Actual Prizes

    🥇The first-place winner will receive a 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. They'll likely be lost in a blissful bubble of iconic audio, completely undisturbed by mere mortals thanks to that renowned noise cancellation. With a relentless, long-lasting battery powering their escape, all neatly packed into a compact, durable design, who can blame them? Participate to avoid the sudden pang of 'earbud envy'.

  11. Hackathon Rules

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  12. Submission Form

    1. Stay Tuned for the Form!

      Roadside Recognition Using HLS

      Build the most energy-efficient inferencing accelerator for edge devices in this 30-day challenge, where victory goes to the team achieving the lowest energy per inference. Whether you prioritize maximum speed, ultra-low power consumption, or find the perfect balance, you'll need to meet strict accuracy and area requirements to claim victory.