Where the starter design’s 8,994 nJ goes
Trace per-layer area, power, time, and energy so the score stops being a mysterious number.
Practical notes on high-level synthesis, hls4ml, Catapult, accelerator architecture, fixed-point arithmetic, and the experiments that connect a neural-network model to hardware.
Trace per-layer area, power, time, and energy so the score stops being a mysterious number.
Use parameter counts, multiplication counts, and repeated training runs to eliminate expensive designs before full hardware synthesis.
Walk through fixed-point width, fractional bits, overflow, saturation, rounding, and the accuracy tradeoff.
Measure the area-versus-time tradeoff instead of assuming that fewer multipliers automatically means lower energy.
Follow the final integration step, record where the design landed, and turn the experiment into a reproducible result.
Publish the complete winning configuration and the lessons from the measured sweep after the contest closes.
These are engineering notes rather than marketing copy. The goal is to explain what changed, what was measured, what surprised me, and which mental model survived contact with the tools. The HLS work here follows the hackathon’s fixed flow: describe the network, train it, convert it to fixed point, synthesize with HLS, measure time and power, and reason from energy as power × time.