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Workshop: Gaussian Processes, Spatiotemporal Modeling, and Decision-making Systems

Surrogate-Assisted Evolutionary Multi-Objective Optimization for Hardware Design Space Exploration

Renzhi Chen · Ke Li


Hardware design space exploration (DSE) aims to find a suitable micro-architecture for the dedicated hardware accelerators. It is a computationally expensive blackbox optimization problem with more than one conflicting performance indicator. Surrogate-assisted evolutionary algorithm is a promising framework for expensive multi-objective optimization problems given its surrogate modeling for handling expensive objective functions and population-based characteristics that search for a set of trade-off solutions simultaneously. However, most, if not all, existing studies mainly focus ‘regular’ Pareto-optimal fronts (PFs), whereas the PF is typically irregular in hardware DSE. In the meanwhile, the gradient information of the differentiable surrogate model(s) is beneficial to navigate a more effective exploration of the search space, but it is yet fully exploited. This paper proposes a surrogate-assisted evolutionary multi-objective optimization based on multiple gradient descent (MGD) for hardware DSE. Empirical results on both synthetic problems with irregular PFs and real-world hardware DSE cases fully demonstrate the effectiveness and outstanding performance of our proposed algorithm.

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