Graph Neural Networks (GNNs) are becoming increasingly popular for vision-based applications due to their intrinsic capacity in modeling structural and contextual relations between various parts of an image frame. On another front, the rising popularity of deep vision-based applications at the edge has been facilitated by the recent advancements in heterogeneous multi-processor Systems on Chips (MPSoCs) that enable inference under real-time, stringent execution requirements. By extension, GNNs employed for vision-based applications must adhere to the same execution requirements. Yet contrary to typical deep neural networks, the irregular flow of graph learning operations poses a challenge to running GNNs on such heterogeneous MPSoC platforms. In this paper, we propose a novel unified design-mapping approach for efficient processing of vision GNN workloads on heterogeneous MPSoC platforms. Particularly, we develop MaGNAS, a mapping-aware Graph Neural Architecture Search framework. MaGNAS proposes a GNN architectural design space coupled with prospective mapping options on a heterogeneous SoC to identify model architectures that maximize on-device resource efficiency. To achieve this, MaGNAS employs a two-tier evolutionary search to identify optimal GNNs and mapping pairings that yield the best performance trade-offs. Through designing a supernet derived from the recent Vision GNN (ViG) architecture, we conducted experiments on four (04) state-of-the-art vision datasets using both (i) a real hardware SoC platform (NVIDIA Xavier AGX) and (ii) a performance/cost model simulator for DNN accelerators. Our experimental results demonstrate that MaGNAS is able to provide 1.57x latency speedup and is 3.38x more energy-efficient for several vision datasets executed on the Xavier MPSoC vs. the GPU-only deployment while sustaining an average 0.11% accuracy reduction from the baseline.

翻译：图神经网络（GNN）因其在建模图像帧各部分间结构与上下文关系方面的内在能力，正日益广泛用于视觉应用。另一方面，异构多处理器片上系统（MPSoC）的最新进展使得在边缘端运行新型深度视觉应用成为可能，这些系统能在实时、严格的执行需求下实现推理。由此延伸，面向视觉应用的GNN也必须遵循相同的执行要求。然而，与典型深度神经网络不同，图学习操作的不规则流程为在异构MPSoC平台上运行GNN带来了挑战。本文提出一种新颖的统一设计-映射方法，以在异构MPSoC平台上高效处理视觉GNN工作负载。具体而言，我们开发了MaGNAS——一种映射感知型图神经架构搜索框架。MaGNAS将GNN架构设计空间与异构SoC上的潜在映射选项相结合，旨在识别能最大化设备端资源效率的模型架构。为此，MaGNAS采用双层进化搜索方法，以识别产生最佳性能权衡的最优GNN与映射配对。通过设计基于最新Vision GNN（ViG）架构的超网络，我们分别在（i）真实硬件SoC平台（NVIDIA Xavier AGX）和（ii）面向DNN加速器的性能/成本模型模拟器上，针对四个（04）主流视觉数据集进行了实验。实验结果表明，在Xavier MPSoC上执行多个视觉数据集时，与仅使用GPU的部署方案相比，MaGNAS可实现1.57倍的延迟加速，能效提升3.38倍，同时较基线仅平均降低0.11%的精度。