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A Comparative Analysis of ARM and x86-64 Laptop-Class Processors: Architecture, Assembly-Level Performance, and Energy Efficiency

Hardware Architecture 2026-04-22 v1

Abstract

ARM-based and x86-64 laptop processors differ not only in instruction-set design, but also in memory hierarchy, core organization, system integration, and power-management mechanisms. This study presents a combined architectural and experimental comparison of an Apple M3 system and an AMD Ryzen 7 3750H system. The architectural analysis contrasts AArch64's fixed-width load-store design with the variable-length, memory-operand-rich x86-64 instruction model, and discusses how register organization, calling conventions, heterogeneous core organization, memory behavior, and low-power mechanisms shape observed performance and energy characteristics. The experimental part uses two native assembly benchmarks: a recursive Fibonacci workload and an integer matrix-multiplication workload. The analysis combines repeated timing measurements, processor-energy measurements, and cross-platform microarchitectural counter measurements from matched portable-C profiling runs. The Ryzen platform is decisively faster on the branch-heavy Fibonacci benchmark, while matrix multiplication shows no meaningful timing advantage for either platform in the present measurements. In contrast, the Apple platform is markedly more energy-efficient, reducing energy-to-solution by approximately 5.82×\times on Fibonacci and 6.38×\times on matrix multiplication. These results are interpreted as platform-level findings rather than as pure ISA-only effects, reflecting differences in implementation, system integration, and measurement methodology in addition to instruction-set structure.

Keywords

Cite

@article{arxiv.2604.18896,
  title  = {A Comparative Analysis of ARM and x86-64 Laptop-Class Processors: Architecture, Assembly-Level Performance, and Energy Efficiency},
  author = {Mustafa Mert Özyılmaz},
  journal= {arXiv preprint arXiv:2604.18896},
  year   = {2026}
}

Comments

13 pages, 3 figures. submitted to Transactions on Computer Science and Applications TCSA