OneDSE: Metric-Conditioned Inverse Modeling and Active Search for Sample-Efficient DSE

Abstract

We identify two key challenges in prior CPU design space exploration (DSE) approaches: (a) short-horizon prediction is forward-only: modeling PPA metrics from design parameters while designers start from metric targets, and (b) long-horizon exploration is slow: evaluating thousands of candidates on cycle-accurate simulators. This work presents OneDSE, which unifies short-horizon design prediction and long-horizon design optimization through Metric-conditioned INverse Design (MIND) and a Surrogate-Assisted Inverse Loop (SAIL). First, OneDSE-MIND inverts the prevailing recipe: conditioned on the workload, it predicts the design that achieves target metrics, finding strong design points in a handful of validations. An information-theoretic analysis supports this inversion approach: the workload observation raises the design information that metrics carry by 12-32% otherwise a workload-blind approach makes richer metrics harder to invert. Second, OneDSE-SAIL embeds MIND in a measurement loop in which fine-tuned inverse proposals drive early sample efficiency while coordinated multi-parameter operators secure the endpoints, under a distance-aware acquisition. Results show that on five TailBench workloads using gem5, MIND reaches, with as few as 1-58 validations, design quality that ArchGym's genetic algorithm needs 11-357x as many evaluations to match (median 68x). Further, SAIL attains a geometric-mean 0.98x the full 6400-evaluation GA optimum with 12.5x fewer online evaluations, exceeding it outright on one workload, versus 0.83x for the strongest budget-matched baseline (SMAC). Finally, we demonstrate generality beyond CPUs by extending OneDSE to design space exploration of a DRAM memory controller and the FEATHER reconfigurable AI accelerator.