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Paper To Do
Adrian Sampson edited this page Aug 1, 2014
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- Intro rework. See the other to-do page. @sampsyo @luisceze
- Ensure we sell ACCEPT's advantages over manual transformation (from PLDI reviews). Probably mention relative broadness of our "deployment" (i.e., undergrads have successfully used this). @sampsyo
- Region selection in Section 4.2. @andreolb
- Neural network "optimization" description in 4.3. @andreolb @tmoreau89
- NPU implementation details in 6.2. @tmoreau89
- Annotation section should emphasize that escape hatches are unsound and intentionally so (something that made PLDI reviewers angry). This is a practical system dealing with imperfect realities. Maybe also place more emphasis training/testing divide to build confidence in results. @sampsyo
- Perspectives from undergrads in eval.
- New title? We currently have "software" but the NPU thing is pretty hardwarey. It would also be nice to emphasize the new "practical" part of the motivation: something you can use today.
- Lots of cuts in 6.2 and 6.3 (too many irrelevant implementation details). @sampsyo
- Experimental setup details for Zynq in 7.2.
- Cut AA relaxation stuff.
- Add NPU results.
- Add Wisp results.
- Collect a new batch of x86 results?
- Add some sort of quantification of the annotation burden?
- Similarly, some sort of quantification of the value of auto-tuning? How many configurations were explored, and how hard was it to find a good one? Would an exhaustive search find anything better (we could do an exhaustive search for one benchmark as a case study)?
- Time allowing: re-introduce and measure "nullify" optimization?
- Error bars in the figure, and possibly statistical tests to show speedup.