  To meet the growing need for computational power for DNNs, multiple
specialized hardware architectures have been proposed. Each DNN layer should be
mapped onto the hardware with the most efficient schedule, however, SotA
schedulers struggle to consistently provide optimum schedules in a reasonable
time across all DNN-HW combinations.
  This paper proposes SALSA, a fast dual-engine scheduler to generate optimal
execution schedules for both even and uneven mapping. We introduce a new
strategy, combining exhaustive search with simulated annealing to address the
dynamic nature of the loop ordering design space size across layers. SALSA is
extensively benchmarked against two SotA schedulers, LOMA and Timeloop on 5
different DNNs, on average SALSA finds schedules with 11.9% and 7.6% lower
energy while speeding up the search by 1.7x and 24x compared to LOMA and
Timeloop, respectively.
