Efficient Transformer Acceleration through Hardware-Aware Neural Network Optimization

Mathew Cervantes; Marisa Mooney; Cintia Nevado; Leandra Figueras

Authors

Mathew Cervantes Department of Computer Science, University of New Hampshire, Durham, NH, USA. Author
Marisa Mooney Department of Electrical Engineering and Computer Science, University of Kansas, Lawrence, KS, USA. Author
Cintia Nevado Department of Computer Science, University of North Texas, Denton, TX, USA. Author
Leandra Figueras Department of Computer Science, University of Houston, Houston, TX, USA. Author

Keywords:

transformer acceleration; hardware-aware optimization; neural architecture search; quantization; edge inference; socio-technical systems; governance; sustainability

Abstract

Transformer-based models have become central to modern artificial intelligence, yet their deployment increasingly confronts constraints in latency, energy, memory bandwidth, and cost. This paper examines efficient transformer acceleration through hardware-aware neural network optimization, treating acceleration not merely as a kernel-level engineering problem but as a socio-technical systems challenge. It synthesizes architectural trends, including sparse attention, hierarchical designs, quantization, pruning, knowledge distillation, and hardware-aware neural architecture search, and relates them to deployment infrastructures such as accelerators, compilers, edge platforms, and datacenter schedulers. The analysis emphasizes structural trade-offs among accuracy, throughput, robustness, fairness, maintainability, and environmental impact. It argues that optimization choices are governance choices because they shape which capabilities are feasible, who can access them, and what risks are externalized. The paper also discusses policy implications for transparency, procurement, sustainability reporting, and equitable service delivery. Through case illustrations and cross-domain comparisons, it proposes a research agenda that integrates performance modeling, lifecycle assessment, and accountable design. The conclusion is that efficient transformer acceleration should be evaluated through multidimensional system metrics rather than single-device speedups alone. Such an approach can improve practical deployment while reducing the risk that efficiency gains obscure concentration of power, hidden technical debt, or uneven social benefit.

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Efficient Transformer Acceleration through Hardware-Aware Neural Network Optimization

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