Reinforcement Learning Cuts Routing Violations in Dense Chip Layouts (NYU)

Researchers at New York University published a technical paper titled “Routing Dense Layouts with History-Aware Offline Reinforcement Learning using LSTM.” Abstract Excerpt: “Detailed routing remains a dominant runtime bottleneck in physical design due to increasing complexity of design rules. Modern routers can struggle to resolve persistent violations under dense operating conditions. While recent work leverages... read more The post Reinforcement Learning Cuts Routing Violations in Dense Chip Layouts (NYU) appeared first on Semiconductor Engineering. ]]>