Energy-Efficient Hardware Acceleration for Graph Neural Network Inference
Keywords:
graph neural networks, hardware acceleration, energy efficiency, inference systems, data movement, sustainable computing, edge deployment, governanceAbstract
Graph neural networks have become a central computational method for learning over relational data, spanning recommendation, fraud detection, drug discovery, transportation forecasting, and scientific simulation. Although much of the research literature has concentrated on algorithmic accuracy and expressive power, inference deployment increasingly depends on whether graph neural network workloads can be executed within strict energy, latency, thermal, and cost envelopes. This paper examines energy-efficient hardware acceleration for graph neural network inference from a system-level perspective. It argues that the dominant challenge is not merely arithmetic throughput but irregular data movement, poor locality, dynamic sparsity, and the mismatch between graph-centric execution and conventional dense accelerator pipelines. The discussion reviews graph neural network workload characteristics, accelerator architectures, sparsity-aware execution, quantization, memory hierarchy design, and deployment infrastructure. It then connects technical choices to operational governance, robustness, fairness, and sustainability. The paper emphasizes that energy-efficient inference requires co-design across algorithms, compilers, runtimes, hardware, facilities, and policy instruments. It also considers edge and data-center deployment, carbon-aware scheduling, hardware reuse, and the risks of optimizing only average-case efficiency while neglecting tail latency or underrepresented graph partitions. The conclusion is that sustainable graph neural network inference will depend on transparent measurement, workload-aware accelerator design, and governance frameworks that treat energy as a first-class systems constraint rather than an afterthought.
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