Wenji Fang

Optimizing register-transfer-level (RTL) code has long relied on manual rewriting, and existing evaluations mostly measure gains on manually degraded designs under weak synthesis flows, so they cannot show whether a rewrite beats a commercial synthesis tool. Dr. RTL closes a loop of timing analysis, parallel rewriting, commercial synthesis and sequential equivalence checking, and distills successful and failed rewrites into reusable pattern-strategy skills. On 20 original human-written RTL designs it improves worst negative slack (WNS) by 21.3% and total negative slack (TNS) by 16.9% on average over commercial synthesis, with a 5.8% area reduction and an average sequential equivalence check pass rate of 86%.

Optimizing register-transfer-level (RTL) code has long relied on manual rewriting, and existing evaluations mostly measure gains on manually degraded designs under weak synthesis flows, so they cannot show whether a rewrite beats a commercial synthesis tool. Dr. RTL closes a loop of timing analysis, parallel rewriting, commercial synthesis and sequential equivalence checking, and distills successful and failed rewrites into reusable pattern-strategy skills. On 20 original human-written RTL designs it improves worst negative slack (WNS) by 21.3% and total negative slack (TNS) by 16.9% on average over commercial synthesis, with a 5.8% area reduction and an average sequential equivalence check pass rate of 86%.