Title: CODA: Learning to Guide Constraint-Aware Optimization for Hardware Accelerators
Primary area: optimization
Keywords: Hardware Accelerator Design, Data-Driven Optimization, Offline Optimization, Evolutionary Computation

Abstract: Designing specialized hardware accelerators is crucial for sustaining the rapid progress of deep learning, yet it remains a costly offline data-driven optimization problem. Evaluating accelerator performance typically requires expensive simulations, while a vast portion of the design space is infeasible due to strict area constraints. Existing data-driven optimization methods often lack feasibility guarantees and suffer from premature convergence on limited offline data. In this paper, we propose the Constrained Offline Design of Accelerators (CODA), a framework for constraint-aware optimization from offline data. CODA tackles data sparsity through a unified surrogate equipped with a cascaded prediction architecture. The model is jointly trained to first predict feasibility and subsequently performance, encouraging the learning of a shared representation that ensures reliable constraint-performance prediction from the limited data. Further, a constraint-aware evolutionary search guided by the surrogate balances exploration and exploitation, accelerating convergence toward feasible high-performance solutions. Extensive experiments on real-world accelerator tasks demonstrate that CODA consistently outperforms both online optimization by 1.11× and surpasses state-of-the-art offline methods by 1.42×. These results highlight CODA as a scalable and robust approach toward automated accelerator design in offline settings. The code is available at https://anonymous.4open.science/r/CODA-38F4/.