Final Project Presentations — Part 2
Rodolfo Azevedo
Institute of Computing, University of Campinas (UNICAMP), Brazil
rodolfo.azevedo@unicamp.br
http://www.ic.unicamp.br/~rodolfo/mo801
Goal of this class
Module 6, Class 3: remaining pairs present. Project 3 due at end of this class.
At the end of this class, you should be able to:
- Deliver a complete 15-minute presentation with live demo or recorded video of your accelerator.
- Explain your roofline analysis and what it reveals about your design's efficiency ceiling.
- Reflect critically on the design decisions you would change with hindsight.
- Evaluate peer presentations through focused technical questions during Q&A.
Format (same as Part 1)
- 15 minutes + 5 minutes Q&A per pair.
- Live demo on Tang Nano 9K or recorded video.
- Same grading criteria as Part 1 (see M06A02).
Presentation schedule
(Filled in at the start of class)
| Slot | Pair | Project title |
|---|---|---|
| 1 | ||
| 2 | ||
| 3 | ||
| 4 | ||
| 5 |
Project 3 submission checklist
Submit via the course platform by the end of this class:
-
src/— all SystemVerilog source files (accelerator + integration changes). -
tb/— Verilator testbench(es): unit test for accelerator, integration test. -
sw/— C driver (accel_driver.c/h) and modifiedkws_kernel.c. -
results/profiling_baseline.txt— M04A03 profiling output (cycles per layer, software). -
results/profiling_accelerated.txt— M05A03 profiling output (cycles per layer, with accelerator). -
results/utilization.txt— nextpnr resource report (make loadoutput). -
results/roofline.md— arithmetic intensity calculation, roofline position, bound identification. -
README.md— design decisions, build instructions, known limitations.
Class discussion: comparing designs
After all presentations, a 15-minute open discussion:
- Which design achieved the highest system speedup? Why?
- Which roofline analysis was most insightful?
- Did anyone find a bug in the profiling stage that changed their design decision?
- What would the class do differently if starting over?
Next class
Retrospective — the full abstraction stack from gates to AI inference, what comes next, and course feedback.