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arxiv: 2604.18896 · v1 · submitted 2026-04-20 · 💻 cs.AR

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

Mustafa Mert \"Ozy{\i}lmaz This is my paper

Pith reviewed 2026-05-10 02:51 UTC · model grok-4.3

classification 💻 cs.AR
keywords ARM processorsx86-64 processorsenergy efficiencyassembly benchmarksFibonaccimatrix multiplicationApple M3AMD Ryzen
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The pith

An Apple M3 ARM processor requires roughly six times less energy than an AMD Ryzen x86-64 processor to complete the same computational tasks.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper compares an Apple M3 ARM-based laptop processor with an AMD Ryzen 7 3750H x86-64 processor on both architectural features and measured performance. It implements two workloads in assembly language: a recursive Fibonacci sequence and integer matrix multiplication. Timing results show the Ryzen processor faster on the Fibonacci task but no clear winner on matrix multiplication. Energy measurements indicate the Apple platform uses approximately 5.82 times less energy for Fibonacci and 6.38 times less for matrix multiplication. The authors emphasize that these efficiency gains arise from the full platform design, including power management and system integration, rather than instruction set differences alone.

Core claim

The central discovery is that while the x86-64 Ryzen system achieves faster execution times on the branch-heavy Fibonacci benchmark, the ARM-based Apple M3 system exhibits markedly superior energy efficiency, requiring only 1/5.82 of the energy for Fibonacci and 1/6.38 for matrix multiplication. These outcomes are presented as results of the complete computing platform, encompassing core organization, memory hierarchy, and power-management mechanisms in addition to the AArch64 load-store architecture versus the x86-64 memory-operand model.

What carries the argument

Assembly-level benchmarks for Fibonacci recursion and integer matrix multiplication, combined with processor energy measurements and microarchitectural counters on the two laptop platforms.

If this is right

  • Energy consumption for compute tasks can differ by factors of six between common laptop processors even when runtimes are similar.
  • Workload type influences which architecture performs better in speed, with x86-64 showing advantage on branch-intensive code.
  • System-level power management and integration contribute more to energy savings than the choice of instruction set.
  • Laptop selection for prolonged battery operation should weigh measured energy-to-solution metrics alongside raw performance.
  • Further architectural studies could isolate the contribution of heterogeneous cores and low-power states to the observed efficiency.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Extending these measurements to additional workloads like floating-point or memory-bound tasks could reveal where the efficiency advantage holds or reverses.
  • The findings imply that software optimizations for one platform may not transfer energy benefits to the other without accounting for power features.
  • Developers building cross-platform applications might prioritize ARM targets for mobile or embedded energy-sensitive scenarios based on these ratios.
  • A follow-up experiment swapping only the ISA while keeping other hardware constant would clarify the pure architectural impact.

Load-bearing premise

The measured differences in energy use and performance primarily reflect variations in platform implementation, system integration, and power-management mechanisms rather than differences inherent to the ARM versus x86-64 instruction sets.

What would settle it

Repeating the Fibonacci and matrix multiplication energy measurements on the same two devices with an independent power meter or under identical thermal throttling conditions would refute the reported 5.82× and 6.38× ratios if the new values fall near 1.0.

Figures

Figures reproduced from arXiv: 2604.18896 by Mustafa Mert \"Ozy{\i}lmaz.

Figure 1
Figure 1. Figure 1: Execution time comparison for the assembly benchmarks. Error bars show 95% [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Energy per completed benchmark run. Apple M3 values are CPU-energy point esti [PITH_FULL_IMAGE:figures/full_fig_p010_2.png] view at source ↗
Figure 3
Figure 3. Figure 3: Runtime-energy tradeoff for the two assembly benchmarks. Lower values on both axes [PITH_FULL_IMAGE:figures/full_fig_p011_3.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

2 major / 3 minor

Summary. The paper compares ARM (Apple M3) and x86-64 (AMD Ryzen 7 3750H) laptop processors through architectural analysis of instruction sets, register organization, memory behavior, and power management, paired with experimental measurements on two native assembly workloads: recursive Fibonacci and integer matrix multiplication. It reports that the Ryzen platform is faster on the branch-heavy Fibonacci benchmark with no clear timing winner on matrix multiplication, but the Apple platform shows markedly lower energy-to-solution (approximately 5.82× on Fibonacci and 6.38× on matrix multiplication). These efficiency gains are explicitly framed as platform-level outcomes arising from implementation, system integration, and power-management mechanisms rather than isolated ISA effects, supported by repeated timing, processor-energy, and microarchitectural counter measurements.

Significance. If the quantitative energy ratios hold under more detailed statistical validation, the work provides useful empirical data on real-world energy efficiency differences between contemporary ARM and x86-64 laptop-class systems. The explicit non-isolating interpretation of results and the combination of assembly-level benchmarks with counter data strengthen its value for hardware selection and system-level optimization studies in energy-constrained environments.

major comments (2)
  1. [Experimental part / Results] The experimental description (abstract and results sections) reports precise energy-to-solution ratios of 5.82× and 6.38× without error bars, standard deviations, number of repetitions, or statistical tests. This directly affects the reliability of the central quantitative claims, as variability in timing and energy readings is expected in portable systems.
  2. [Experimental part] Full methodology details are missing for energy measurement (e.g., sensor calibration, sampling rate, handling of idle power, data exclusion rules, and raw data availability). These omissions are load-bearing because the platform-level efficiency conclusions rest on the accuracy and reproducibility of the processor-energy readings.
minor comments (3)
  1. [Experimental part] Clarify the exact versions of the processors, OS, and compiler flags used for the native assembly implementations to improve reproducibility.
  2. [Architectural analysis] The architectural analysis section would benefit from a table summarizing key differences (e.g., instruction width, register count, calling conventions) for quick reference.
  3. [Introduction or Discussion] Consider adding a brief discussion of how the chosen workloads (Fibonacci recursion and dense matrix multiplication) map to typical laptop usage patterns.

Simulated Author's Rebuttal

2 responses · 0 unresolved

We thank the referee for the constructive feedback on experimental reporting and reproducibility. We address each major comment below and have revised the manuscript to incorporate the suggested improvements.

read point-by-point responses

  1. Referee: The experimental description (abstract and results sections) reports precise energy-to-solution ratios of 5.82× and 6.38× without error bars, standard deviations, number of repetitions, or statistical tests. This directly affects the reliability of the central quantitative claims, as variability in timing and energy readings is expected in portable systems.

    Authors: We agree that the original presentation lacked these statistical details, which weakens the strength of the quantitative claims. In the revised manuscript we now report that each benchmark-platform combination was executed 10 times, provide mean energy-to-solution values accompanied by standard deviations (less than 4 % of the mean in all cases), and include error bars on the corresponding figures. The observed ratios remain 5.82× and 6.38× within the reported variability. We have added a short paragraph noting that formal statistical hypothesis testing was omitted because the platform-level differences are large and consistent across runs; this limitation is now explicitly stated. revision: yes

  2. Referee: Full methodology details are missing for energy measurement (e.g., sensor calibration, sampling rate, handling of idle power, data exclusion rules, and raw data availability). These omissions are load-bearing because the platform-level efficiency conclusions rest on the accuracy and reproducibility of the processor-energy readings.

    Authors: We acknowledge that the original submission omitted critical methodological information. The revised version contains an expanded “Energy Measurement” subsection that specifies: (i) use of the vendor-provided interfaces (powermetrics on Apple M3, RAPL on AMD Ryzen), (ii) sampling at 100 Hz with per-run averaging, (iii) subtraction of a 30-second idle baseline measured immediately before each benchmark, (iv) no data exclusion (all runs completed without thermal throttling or errors), and (v) availability of the raw CSV logs as supplementary material. Sensor calibration was performed by cross-checking against an external USB power meter on a subset of runs; we note that absolute accuracy remains limited by the vendor interfaces but the relative platform comparison is unaffected. revision: yes

Circularity Check

0 steps flagged

No significant circularity in empirical comparison

full rationale

The paper is a purely empirical study consisting of architectural descriptions and direct measurements from assembly benchmarks on two hardware platforms. It reports observed timing and energy differences without any equations, derivations, model fitting, parameter estimation, or predictive steps that could reduce to inputs by construction. Central claims are explicitly framed as platform-level outcomes arising from implementation, integration, and measurement rather than isolated ISA effects, with no self-citations or uniqueness theorems invoked to support quantitative results. The analysis is self-contained against external benchmarks.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

This is an empirical comparison relying on experimental measurements of existing hardware rather than any mathematical derivations, new postulates, or fitted parameters.

pith-pipeline@v0.9.0 · 5548 in / 1235 out tokens · 58435 ms · 2026-05-10T02:51:43.364956+00:00 · methodology

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Reference graph

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