  Industry has gradually moved towards application-specific hardware
accelerators in order to attain higher efficiency. While such a paradigm shift
is already starting to show promising results, designers need to spend
considerable manual effort and perform a 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 set of
target applications or design 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 simulations. Such
an approach not only alleviates the need to run time-consuming simulation, but
also enables data reuse and applies even when set of target applications
changes. In this paper, we develop such a data-driven offline optimization
method for designing hardware accelerators, dubbed 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. PRIME architects accelerators -- tailored towards both single and
multiple applications -- improving performance upon state-of-the-art
simulation-driven methods by about 1.54x and 1.20x, while considerably reducing
the required total simulation time by 93% and 99%, respectively. In addition,
PRIME also architects effective accelerators for unseen applications in a
zero-shot setting, outperforming simulation-based methods by 1.26x.
