Nexus of Sampling, Sequential Decision-making, L2O and Cloud Today’s IC design optimizations were developed for pre-cloud era compute infrastructure of the 1980s and 1990s. We seek to reinvent CAD optimizations in a {sampling, L2O, federated, cloud} context. We apply distributed sampling and sequential decision-making methods to core optimizations, starting from detailed routing and concurrent placement […]
Modern Hypergraph Partitioning Balanced hypergraph partitioning is a well-studied, fundamental combinatorial optimization problem with multiple applications in EDA. The objective is to partition vertices of a hypergraph into a specified number of disjoint blocks such that each block has bounded size and the cutsize, i.e., the number of hyperedges spanning multiple blocks, is minimized. In […]
Optimal Embedding: HypOp Combinatorial optimization is ubiquitous across science and industry. In recent years, the integration of artificial intelligence (AI) into the field of scientific discovery is growing increasingly fluid, providing means to enhance and accelerate research. An approach to integrate AI into scientific discovery involves leveraging machine learning (ML) methods to expedite and improve […]
Optimal Embedding Embedding refers to the placement and routing of a netlist hypergraph into a 3-D chip layout, as well as layout-related optimizations to minimize area, power and delay metrics while satisfying layout constraints (design rules), etc. In this topic, the ultimate goal is to scale the capacity and speed of optimal and near-optimal solvers […]
