映射和征服：动态神经网络到异构MPSoCs的节能映射
Map-and-Conquer: Energy-Efficient Mapping of Dynamic Neural Nets onto Heterogeneous MPSoCs
摘要：
异构MPSoC包括不同计算能力的不同处理单元。迄今为止，神经网络（NN）到这样的系统的映射策略尚未开发的处理并行性的全部潜力，通过内在NN的结构和底层硬件组成。在本文中，我们提出了一种新的框架，有效地映射到异构MPSoC的方式，使他们能够利用底层的处理并发神经网络。具体来说，我们的方法确定了最佳的分割方案的NN沿其“宽度”的维度，这有利于并发NN块部署到不同的硬件计算单元。此外，我们的方法有助于一个新的计划，部署分区NN到MPSoC作为动态多出口网络的额外的性能增益。我们在标准MPSoC平台上的实验已经产生了比仅GPU映射高2.1倍的能效的动态映射配置，同时比仅DLA映射少1.7倍的延迟。
英文摘要：
Heterogeneous MPSoCs comprise diverse processing units of varying compute capabilities. To date, the mapping strategies of neural networks (NNs) onto such systems are yet to exploit the full potential of processing parallelism, made possible through both the intrinsic NNs' structure and underlying hardware composition. In this paper, we propose a novel framework to effectively map NNs onto heterogeneous MPSoCs in a manner that enables them to leverage the underlying processing concurrency. Specifically, our approach identifies an optimal partitioning scheme of the NN along its `width' dimension, which facilitates deployment of concurrent NN blocks onto different hardware computing units. Additionally, our approach contributes a novel scheme to deploy partitioned NNs onto the MPSoC as dynamic multi-exit networks for additional performance gains. Our experiments on a standard MPSoC platform have yielded dynamic mapping configurations that are 2.1x more energy-efficient than the GPU-only mapping while incurring 1.7x less latency than DLA-only mapping.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
