Neural Architecture Search for Efficient Deep Learning Model Deployment
Keywords:
neural architecture search, efficient deployment, machine learning systems, edge computing, model governance, sustainability, robustness, fairness, infrastructure, policyAbstract
Neural architecture search has emerged as a central technique for automating the design of deep learning models, yet its most consequential implications lie not merely in predictive accuracy but in the system-level conditions of deployment. This paper examines neural architecture search as a socio-technical infrastructure problem spanning model discovery, hardware constraints, software toolchains, operational governance, sustainability, fairness, and policy. It argues that efficient deployment cannot be reduced to smaller models or faster inference; rather, it requires coordinated decisions across architecture search spaces, latency and energy budgets, compiler and runtime support, edge and cloud topologies, monitoring regimes, and institutional accountability. The analysis positions neural architecture search within a broader trajectory of automated machine learning and systems co-design, showing how search objectives shape not only model quality but also maintenance burden, carbon intensity, equity of access, and robustness under distribution shift. Through cross-domain comparisons in mobile vision, natural language processing, autonomous systems, and federated environments, the paper identifies structural trade-offs between adaptability, heterogeneity, privacy, and cost. It also considers how benchmarking, model documentation, procurement standards, and regulatory expectations influence whether searched architectures become reliable public infrastructure or opaque technical debt. The conclusion emphasizes that efficient deployment is a governance as well as an engineering achievement. Without explicit attention to lifecycle evaluation, transparency, and institutional responsibility, neural architecture search may optimize narrow metrics while externalizing risks to users, operators, and the environment.
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