Along with the fast evolution of deep neural networks, the hardware system is
also developing rapidly. As a promising solution achieving high scalability and
low manufacturing cost, multi-accelerator systems widely exist in data centers,
cloud platforms, and SoCs. Thus, a challenging problem arises in
multi-accelerator systems: selecting a proper combination of accelerators from
available designs and searching for efficient DNN mapping strategies. To this
end, we propose MARS, a novel mapping framework that can perform
computation-aware accelerator selection, and apply communication-aware sharding
strategies to maximize parallelism. Experimental results show that MARS can
achieve 32.2% latency reduction on average for typical DNN workloads compared
to the baseline, and 59.4% latency reduction on heterogeneous models compared
to the corresponding state-of-the-art method.

基于深度神经网络的多加速器系统中，选择适当的加速器组合和高效的神经网络映射策略是一个具有挑战性的问题，我们提出了一个名为 MARS 的新型映射框架，可以执行计算感知的加速器选择，并应用通信感知的分片策略来最大化并行性，实验证明，与基准相比，MARS 可以平均减少 32.2% 的延迟，并且相对于对应的最先进方法，可以减少 59.4% 的异构模型的延迟。