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Data-Driven Offline Optimization for Architecting Hardware Accelerators

Aviral Kumar
Sergey Levine
International Conference on Learning Representations 2022 (to appear)
Google Scholar


With the goal of achieving higher efficiency, the semiconductor industry has gradually reformed towards application-specific hardware accelerators. While such a paradigm shift is already starting to show promising results, designers need to spend considerable manual effort and perform large number of time-consuming simulations to find accelerators that can accelerate multiple target applications while obeying design constraints. Moreover, such a ``simulation-driven'' approach must be re-run from scratch every time the target applications or constraints change. An alternative paradigm is to use a ``data-driven'', offline approach that utilizes logged simulation data, to architect hardware accelerators, without needing any form of simulation. Such an approach not only alleviates the need to run time-consuming simulation, but also enables data reuse and applies even when target applications change. In this paper, we develop such a data-driven offline optimization method for designing hardware accelerators, PRIME, that enjoys all of these properties. Our approach learns a conservative, robust estimate of the desired cost function, utilizes infeasible points and optimizes the design against this estimate without any additional simulator queries during optimization.