MaGNAS: A Mapping-Aware Graph Neural Architecture Search Framework for
  Heterogeneous MPSoC Deployment
Mohanad Odema Halima Bouzidi Hamza Ouarnoughi Smail Niar Mohammad Abdullah Al Faruque
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.