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.

本研究提出了一种名为 SALSA 的快速双引擎调度程序，以支持 DNNs 的最优执行时间，并结合模拟退火的策略解决了循环排序设计空间大小的动态性问题。SALSA 在 5 种不同的 DNN 上与两个 SotA 调度器 LOMA 和 Timeloop 进行了广泛的基准测试，平均而言，与 LOMA 相比，SALSA 的能源降低了 11.9％，搜索速度提高了 1.7 倍，与 Timeloop 相比能源降低了 7.6％ ，搜索速度提高了 24 倍。