arXiv subjects

Owen Randall

Publications and source records attributed to Owen Randall.

3 recordsLinked to original sources

arXiv2026

A PPA-Driven 3D-IC Partitioning Selection Framework with Surrogate Models

3D-IC netlist partitioning is commonly optimized using proxy objectives, while final PPA is treated as a costly evaluation rather than an optimization signal. This proxy-driven paradigm makes it difficult to reliably translate additional PPA evaluations into better PPA outcomes. To bridge this gap, we present DOPP (D-Optimal PPA-driven partitioning selection), an approach that bridges the gap between proxies and true PPA metrics. Across eight 3D-IC designs, our framework improves PPA over Open3DBench (average relative improvements of 9.99% congestion, 7.87% routed wirelength, 7.75% WNS, 21.85% TNS, and 1.18% power). Compared with exhaustive evaluation over the full candidate set, DOPP achieves comparable best-found PPA while evaluating only a small fraction of candidates, substantially reducing evaluation cost. By parallelizing evaluations, our method delivers these gains while maintaining wall-clock runtime comparable to traditional baselines.

cs.LG↗

arXiv2025

A Proof of the 2004 Albert-Grossman-Nowakowski-Wolfe Conjecture on Alternating Linear Clobber

Clobber is an alternate-turn two-player game introduced in 2001 by Albert, Grossman, Nowakowski and Wolfe. The board is a graph with each node colored black (x), white (o), or empty (-). Player Left has black stones, player Right has white stones. On a turn, a player takes one of their stones that is adjacent to an opponent stone and clobbers the opponent's stone (replaces it with theirs). Whoever cannot move loses. Linear clobber is clobber played on a path, for example, one row of a Go board. In 2004 Albert et al. conjectured that, for every even-length alternating-color linear clobber position except oxoxox, the first player has a winning strategy. We prove their conjecture.

math.CO↗

arXiv2024

Expected Work Search: Combining Win Rate and Proof Size Estimation

We propose Expected Work Search (EWS), a new game solving algorithm. EWS combines win rate estimation, as used in Monte Carlo Tree Search, with proof size estimation, as used in Proof Number Search. The search efficiency of EWS stems from minimizing a novel notion of Expected Work, which predicts the expected computation required to solve a position. EWS outperforms traditional solving algorithms on the games of Go and Hex. For Go, we present the first solution to the empty 5x5 board with the commonly used positional superko ruleset. For Hex, our algorithm solves the empty 8x8 board in under 4 minutes. Experiments show that EWS succeeds both with and without extensive domain-specific knowledge.

cs.AI↗