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.

本文提出了 MaGNAS，一个设计 - 映射方法，用于在异构 MPSoC 平台上高效处理图形神经网络工作负载，旨在实现最大化设备资源效率，并证明实验结果表明该方法能够提供 1.57 倍的延迟加速和 3.38 倍的能源效率，而准确率损失仅为 0.11％。

MaGNAS: 一种面向异构 MPSoC 部署的映射感知图神经元架构搜索框架

MaGNAS: A Mapping-Aware Graph Neural Architecture Search Framework for  Heterogeneous MPSoC Deployment

The rapid evolution of Graph Neural Networks (GNNs) has led to a growing
number of new architectures as well as novel applications. However, current
research focuses on proposing and evaluating specific architectural designs of
GNNs, as opposed to studying the more general design space of GNNs that
consists of a Cartesian product of different design dimensions, such as the
number of layers or the type of the aggregation function. Additionally, GNN
designs are often specialized to a single task, yet few efforts have been made
to understand how to quickly find the best GNN design for a novel task or a
novel dataset. Here we define and systematically study the architectural design
space for GNNs which consists of 315,000 different designs over 32 different
predictive tasks. Our approach features three key innovations: (1) A general
GNN design space; (2) a GNN task space with a similarity metric, so that for a
given novel task/dataset, we can quickly identify/transfer the best performing
architecture; (3) an efficient and effective design space evaluation method
which allows insights to be distilled from a huge number of model-task
combinations. Our key results include: (1) A comprehensive set of guidelines
for designing well-performing GNNs; (2) while best GNN designs for different
tasks vary significantly, the GNN task space allows for transferring the best
designs across different tasks; (3) models discovered using our design space
achieve state-of-the-art performance. Overall, our work offers a principled and
scalable approach to transition from studying individual GNN designs for
specific tasks, to systematically studying the GNN design space and the task
space. Finally, we release GraphGym, a powerful platform for exploring
different GNN designs and tasks. GraphGym features modularized GNN
implementation, standardized GNN evaluation, and reproducible and scalable
experiment management.

该研究定义并系统研究了图神经网络的 315,000 种不同建模方式和 32 种预测任务的设计空间，得出了 GNN 设计准则，并提出 GNN 任务空间和相似度度量方法以便于在新任务和数据集上快速找到最佳的 GNN 设计。需要注意的是 GraphGym 也是一种开放且强大的探索不同 GNN 设计和任务的平台。