Hardware-Aware Parallel Ranking Algorithms for Real-Time Neural Network Inference
Keywords:
hardware-aware algorithms; parallel ranking; neural network inference; real-time systems; top-k selection; accelerator architecture; socio-technical infrastructure; AI governance; sustainability; fairnessAbstract
Real-time neural network inference increasingly depends on ranking operations that select, sort, and rescore candidate sets under strict latency, throughput, memory, and energy constraints. This paper examines hardware-aware parallel ranking algorithms as a socio-technical infrastructure problem rather than merely a kernel optimization challenge. It analyzes how row-wise top-k selection, warp-level coordination, memory coalescing, and heterogeneous accelerator scheduling shape the feasibility of real-time recommendation, search, anomaly detection, and decision support. The discussion connects algorithmic design to deployment architectures, including edge devices, data center accelerators, and hybrid cloud-edge pipelines. It argues that ranking primitives are consequential because they determine which items, alerts, or opportunities become visible in high-stakes environments. The paper also considers governance, fairness, robustness, and policy implications when low-latency ranking is embedded in public and commercial systems. Sustainability concerns arise from energy intensity, hardware refresh cycles, and the hidden costs of maintaining low-latency service levels. Through cross-domain comparisons and forward-looking analysis, the paper proposes that hardware-aware ranking should be evaluated by accuracy, tail latency, energy efficiency, maintainability, auditability, and distributional impact. The central claim is that real-time neural inference systems require co-design across algorithms, accelerators, data pipelines, and institutional oversight. Without this integration, optimized kernels may improve throughput while worsening fairness, robustness, or environmental externalities. The paper concludes with research directions for resilient, measurable, and accountable real-time ranking infrastructure.
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