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Tuesday, November 10, 2026 | 9:00 AM PST

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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 participants built an efficient inferencing accelerator.

This hackathon has ended; winners announced below!

Russell Klein Matthew Bone

  1. Winners and time limit

    1. Winners and time limit

      The winning team built the most efficient inferencing system, that delivered predictions with the smallest amount of energy per inference. The implementation met strict performance, accuracy, and area requirements, too.

      Oh, and there was a time limit. Everyone had 30 days to complete their masterpiece of efficient engineering.

      If you participated, check out the runners-up leaderboard below, because you might’ve not made top 10 but your participation still counts!

  2. Final Leaderboard

    1. Leaderboard

  3. Runners-up Leaderboard

    1. Runners-up Leaderboard Table

      RankTeam NamesNanojoules
      11.ALDALab13.465
      12.Dadhichi14.230
      13.Matthias15.812
      14.Del-op20.115
      15.ASL_Ishtiaaq21.685
      16.Juhaer Mahtab22.100
      17.Nirnoy Edge+28.602
      18.HLS Wizard29.000
      19.Sadman115.000
      20.G-Force659.217
      21.ByteMonks4645.000
      22.Lazy Hardware Engineers6878.000
      23.Philip J. Fry12259.000
      24.Software Rulez!145324430.000

  4. Watch: How the Winning Team Did It

    1. Winners Video

      • 0:00

  5. Podcast Finale: Hackathon Results & Wrap-Up

    1. Winners announced!

      Episode 5

      Look back at the journey of the 2026 HLS Summer Hackathon as our hosts do a deep-dive analysis of the final entries, exploring the creative architectures that allowed teams to drop from thousands of NanoJoules down to the winning benchmarks.

      Play Now!

  6. Leaderboard Insights

    2026 Summer Hackathon Podcast

    Catch Russell Klein and Matthew Bone on our hackathon podcast that covers submissions, tips, and exclusive updates through the course of the High-Level Synthesis 2026 summer hackathon.

    Listen Now

  7. The Algorithm – roadside recognition

    We used an object recognition algorithm. That recognized ten kinds of objects found on the side of the road. It processed an input of a 20x20 pixel image and tried to match it to one of ten categories.

    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, participants were able to get through more design iterations faster. Which they needed to zero in on the optimal architecture.

  8. The Starting Line

    Everyone was given a virtual machine equipped with all the tools and IP they would’ve need to build and characterize an ASIC implementation of their inferencing accelerator, courtesy of Siemens EDA. They started with RocketCore RISC-V design, and a bare metal application that runs the recognition algorithm. The job was to make the inference run faster than any software implementation could possibly go, all while the design sips tiny amounts of energy to get the job done.

  9. Objectives Title

    Objectives

  10. Objectives image

  11. Objectives Conclusion

    Was your strategy being 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? Check the leaderboard up top to see how the designs compared.

  12. Do you have what it takes

    1. Did you have what it takes?

      Winning this hackathon required 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.

  13. Winning Criteria

    1. Winning Criteria Recap

      The winning design was the one that met the accuracy, performance, and area criteria, and consumed the least average energy per inference. Participants used PowerPro™ software from Siemens EDA to measure the power of the combined hardware and software system.

  14. Prizes, Trophies, and Bragging Rights

    All participants who completed the hackathon with a valid submission received a badge suitable for promoting their engineering genius on LinkedIn. If you made it either of the leaderboards, have you posted it up on LinkedIn that you won, the High-Level Synthesis low-energy inferencing hackathon?

    Actual Prizes

    🥇The first-place winner got a Elegoo’s Neptune 3D printer, and an opportunity to speak at the Edge AI foundation’s Fall Taipei event (physically or virtually). Literally, and we literally mean this “literally,” fame and fortune.

    🥈The second-place winner got 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 can’t hear you because they’re enjoying their Bose QuietComfort Earbuds. They're likely 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? If you're feeling a sudden pang of 'earbud envy,' we totally get it!

    Make sure to check HLS Academy often; you might be the one tuning out the world (and your colleagues) at our next hackathon. 😉