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

TL;DR

This paper compares ARM and x86-64 laptop processors in architecture, performance, and energy efficiency through experiments on Apple M3 and AMD Ryzen 7 systems, highlighting differences in design and efficiency.

Contribution

It provides a combined architectural and experimental comparison of ARM and x86-64 laptops, emphasizing how design differences impact performance and energy use.

Findings

Ryzen is faster on branch-heavy Fibonacci benchmark.

Apple platform is more energy-efficient, reducing energy-to-solution by over 5-6 times.

Performance differences are influenced by implementation and system integration, not just ISA.

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.