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273 | 273 | \begin{abstract} |
274 | 274 | % An abstract should consist of six main sentences: |
275 | 275 | % 1. Introduction. In one sentence, what’s the topic? |
276 | | -Datapath circuits are the core computational units of the digital designs; |
| 276 | +Datapath circuits are the core computational units of \sam[the]{} digital designs; |
277 | 277 | being the most timing-critical part of the design, they are aggressively optimized. |
278 | 278 | % 2. State the problem you tackle. |
279 | 279 | These aggresive optimizations, such as application of compression trees, make datapath circuits hard to verify. |
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287 | 287 | Our insight is that these methods attempt to tackle the verification problem at the wrong stage. |
288 | 288 | They are all post-synthesis, where the structure of the circuit optimization has already been lost. |
289 | 289 | Instead, we propose a pre-synthesis approach, where we build a formally verified |
290 | | -synthesis pipeline built on the interactive theorem proving. |
| 290 | +synthesis pipeline built on \sam[the]{} interactive theorem proving. |
291 | 291 | This eliminates the need for an expensive post-synthesis verification, |
292 | 292 | instead leveraging the ability to implement correct-by-construction algorithms in a theorem prover. |
293 | 293 | % 5. In one sentence, how did you go about doing the research that follows from your big idea. |
294 | 294 | For this approach, we phrase circuit synthesis as a bit-heap compression problem. |
295 | | -We formalize bit heap data structure and its operations in the Lean theorem prover, |
| 295 | +We formalize \sam{the} bit heap data structure and its operations in the Lean theorem prover, |
296 | 296 | synthesize compression trees with this data structure, |
297 | | -and prove the correctness of these optimizations and the datapath synthesis as a whole. |
| 297 | +and prove the correctness of these optimizations and \sam[the]{} datapath synthesis as a whole. |
298 | 298 | % Has real numbers |
299 | 299 | Furthermore, our framework produces circuits that are consumed by the CIRCT framework, |
300 | 300 | that when applied to real world circuit designs, are \SynthesisSpeedupGeomean{} faster |
301 | | -in synthesis plus verification time on state-of-the-art verificiation tools, |
| 301 | +in synthesis plus verification time \sam[on]{grammar feels off} state-of-the-art verificiation tools, |
302 | 302 | while only being \PerformanceSlowdownGeomean{} slower than the state of the art synthesis. |
303 | 303 | % 6. As a single sentence, what’s the key impact of your research? |
304 | 304 | This demonstrates that our approach provides orders of magnitude speedup on verification, |
305 | | -while not sacrificing performance. |
306 | | -Overall, our approach lays down the foundations for fast, performant datapath circuit synthesis. |
| 305 | +while not \sam[sacrificing performance]{Runtime performance yes - circuit quality which feels like a measure of perf for a synthesis tool - I would not claim.}. |
| 306 | +Overall, our approach lays down the foundations for fast, performant datapath circuit synthesis.\sam{Surely the focus is correct/verified not fast and performant - what does performant mean here - before it meant runtime now it means circuit quality?} |
307 | 307 | % (http://www.easterbrook.ca/steve/2010/01/how-to-write-a-scientific-abstract-in-six-easy-steps/) |
308 | 308 |
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309 | 309 | \end{abstract} |
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