  Synthetic aperture radar (SAR) automatic target recognition (ATR) is the key
technique for remote-sensing image recognition. The state-of-the-art
convolutional neural networks (CNNs) for SAR ATR suffer from \emph{high
computation cost} and \emph{large memory footprint}, making them unsuitable to
be deployed on resource-limited platforms, such as small/micro satellites. In
this paper, we propose a comprehensive GNN-based model-architecture {co-design}
on FPGA to address the above issues. \emph{Model design}: we design a novel
graph neural network (GNN) for SAR ATR. The proposed GNN model incorporates
GraphSAGE layer operators and attention mechanism, achieving comparable
accuracy as the state-of-the-art work with near $1/100$ computation cost. Then,
we propose a pruning approach including weight pruning and input pruning. While
weight pruning through lasso regression reduces most parameters without
accuracy drop, input pruning eliminates most input pixels with negligible
accuracy drop. \emph{Architecture design}: to fully unleash the computation
parallelism within the proposed model, we develop a novel unified hardware
architecture that can execute various computation kernels (feature aggregation,
feature transformation, graph pooling). The proposed hardware design adopts the
Scatter-Gather paradigm to efficiently handle the irregular computation
{patterns} of various computation kernels. We deploy the proposed design on an
embedded FPGA (AMD Xilinx ZCU104) and evaluate the performance using MSTAR
dataset. Compared with the state-of-the-art CNNs, the proposed GNN achieves
comparable accuracy with $1/3258$ computation cost and $1/83$ model size.
Compared with the state-of-the-art CPU/GPU, our FPGA accelerator achieves
$14.8\times$/$2.5\times$ speedup (latency) and is $62\times$/$39\times$ more
energy efficient.
