arxiv: 2605.01793 · v1 · submitted 2026-05-03 · 💻 cs.ET · physics.app-ph

Recognition: unknown

Analytic Framework for Estimating Memory Cost

Anirudh Shankar , Avhishek Chatterjee , Anjan Chakravorty

Authors on Pith no claims yet

Pith reviewed 2026-05-09 16:01 UTC · model grok-4.3

classification 💻 cs.ET physics.app-ph

keywords AI energy consumptionmemory cost estimationecological footprintLLMsDNNssustainable AIanalytic framework

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The pith

A generalized analytic framework quantifies the memory energy costs of AI models and their environmental impact.

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

The paper introduces a generalized framework to estimate the energy consumption caused by memory usage during training and inference of AI models such as large language models and deep neural networks. This matters because growing AI systems drive large data-center power demands and carbon emissions. The framework supplies an analytic way to measure these hidden costs without requiring detailed model internals. If the approach holds, it supplies a basis for comparing designs and steering future AI toward lower environmental footprints.

Core claim

The authors present a generalized analytic framework that quantifies the energy costs incurred to the environment from the massive memory consumption of AI models in data centers. This framework provides a foundational quantification of AI's ecological footprint and thereby facilitates the development of sustainable architectural strategies for future models.

What carries the argument

The generalized analytic framework for estimating memory-driven energy costs, which converts data-center memory usage into environmental energy expenditure across diverse AI models.

If this is right

Architects can compare the environmental impact of alternative AI model designs using a common metric.
Memory-efficient techniques can be evaluated for their effect on overall carbon footprint.
Sustainable strategies for future models can be chosen with quantified memory cost data in hand.
Policy and optimization decisions gain a baseline for reducing AI's energy demands in data centers.

Where Pith is reading between the lines

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

The same framework could be adapted to track energy costs of other hardware resources such as interconnects or accelerators.
Applying the estimates to concrete models like current LLMs would allow direct ranking of their relative footprints.
Data-center hardware designers might use the outputs to prioritize memory technologies that lower total energy draw.
The approach opens a path to integrate memory-cost tracking into existing AI training pipelines for real-time feedback.

Load-bearing premise

A single generalized framework can accurately estimate memory energy costs for many different AI models using only analytic relations and without model-specific details or empirical checks.

What would settle it

Run a known large language model training job, measure its actual energy use, and compare the result against the framework's estimate for the same workload; a large mismatch would show the framework fails to capture real costs.

Figures

Figures reproduced from arXiv: 2605.01793 by Anirudh Shankar, Anjan Chakravorty, Avhishek Chatterjee.

**Figure 1.** Figure 1: Cost Analysis for a Single Dipole This suggests that if the replenishment cost, C(R), is greater than a certain value (given by C(R0)) for a particular external H field, application of an external H field is more energy efficient than the case without the application of an external H field. Intuitively, this can be understood as follows. When the applied external H field is positive, the retention time is … view at source ↗

**Figure 3.** Figure 3: shows the set of curves obtained for constraint (16) for several sf and H values. For any curve, the region above the curve represents the condition C(R) > C(R0) = 1 3 sfCM τ5τ6 τ6−τ5 . If the replenishment cost C(R) lies in this region, it is energy efficient to couple the dipoles in a triangle rather than a linear array at that particular sf and H value. As the external H field increases, the critical re… view at source ↗

read the original abstract

As artificial intelligence (AI) models quickly spread and become more advanced, they are requiring an ever-increasing amount of data and compute capability, leading to a significant energy cost. Training and inference of AI models including the large language models (LLMs) and deep neural networks (DNNs) are contributing to a large carbon footprint owing to the massive amount of memory they consume in data centers. In this article, we present a generalized framework that quantifies these energy costs incurred to the environment. This framework provides a foundational quantification of AI's ecological footprint, facilitating the development of sustainable architectural strategies for future models.

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.

Desk Editor's Note private letter to a colleague

The paper claims a generalized analytic framework for AI memory energy costs but supplies no equations, methods, or evidence.

read the letter

The main takeaway is that this paper states it has developed a generalized analytic framework to quantify the environmental energy costs from memory use in AI models, particularly LLMs and DNNs, but it does not include the framework itself. What it does reasonably is highlight the significant carbon footprint from the massive memory consumption during training and inference in data centers. This is a valid concern in sustainable computing. The real issue is the absence of any technical content. There are no analytic expressions, no discussion of assumptions like how memory access translates to energy, no application to specific models, and no comparison to existing estimation techniques. This makes it impossible to judge if the approach is new or sound. The stress-test concern holds up: without these elements, the claim that the framework accurately estimates costs across diverse models while remaining general cannot be evaluated. It reduces to a high-level assertion. This work might draw interest from readers in the sustainable AI subfield who want to see ongoing attention to ecological impacts. However, it provides no usable tool or insight for developing strategies or for further research. I would not bring it to a reading group or cite it. It does not warrant peer review, as there is no material for referees to assess.

Referee Report

1 major / 0 minor

Summary. The manuscript claims to present a generalized analytic framework for quantifying the energy costs incurred by memory consumption in AI models, including LLMs and DNNs, during training and inference. This framework is asserted to provide a foundational quantification of AI's ecological footprint and to facilitate sustainable architectural strategies for future models.

Significance. The topic of AI's environmental impact is timely and important. However, because the manuscript supplies no equations, derivations, assumptions, abstraction mechanisms, test cases, or validation, the claimed framework cannot be evaluated and contributes nothing to the literature in its current form.

major comments (1)

Abstract: The central claim is that a 'generalized framework' exists which quantifies memory-derived energy costs across models without model-specific inputs or empirical checks. No equations, memory-to-energy conversion rules, abstraction steps, or examples are provided, so the claim that the framework is analytic, general, and predictive cannot be assessed.

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their review. We agree that the current manuscript is a high-level outline and does not supply the equations, derivations, assumptions, or examples needed to evaluate the claimed analytic framework.

read point-by-point responses

Referee: Abstract: The central claim is that a 'generalized framework' exists which quantifies memory-derived energy costs across models without model-specific inputs or empirical checks. No equations, memory-to-energy conversion rules, abstraction steps, or examples are provided, so the claim that the framework is analytic, general, and predictive cannot be assessed.

Authors: We agree that the submitted manuscript does not contain the technical content required to substantiate the abstract's claims. The version under review functions as a conceptual overview rather than a complete technical exposition. We will revise the manuscript to include the full analytic framework: the governing equations for memory energy estimation, the abstraction steps that enable generalization across models without model-specific inputs, the memory-to-energy conversion rules and assumptions, and at least one worked example with comparison to known empirical values. These additions will make the framework evaluable and will directly address the referee's concerns. revision: yes

Circularity Check

0 steps flagged

No derivation chain or equations exhibited; framework asserted at high level only

full rationale

The paper's abstract and provided text assert the presentation of a 'generalized framework that quantifies these energy costs' for AI models' memory consumption but supply no equations, assumptions, derivations, parameter fittings, or citations. Without any visible analytic steps or load-bearing claims that reduce to inputs, no instances of self-definitional logic, fitted predictions renamed as results, or self-citation chains exist to evaluate. The central claim remains an unevaluated assertion rather than a derivation that could be circular.

Axiom & Free-Parameter Ledger

0 free parameters · 0 axioms · 0 invented entities

The abstract provides no equations, parameters, or assumptions, so no free parameters, axioms, or invented entities can be identified.

pith-pipeline@v0.9.0 · 5393 in / 990 out tokens · 35621 ms · 2026-05-09T16:01:36.408079+00:00 · methodology

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Reference graph

Works this paper leans on

24 extracted references · 9 canonical work pages · 1 internal anchor

[1]

Carbon Emissions and Large Neural Network Training

David Patterson, Joseph Gonzalez, Quoc Le, Chen Liang, Lluis-Miquel Munguia, Daniel Rothchild, David So, Maud Texier, and Jeff Dean. Carbon emissions and large neural network training, 2021. URL https://arxiv.org/ab s/2104.10350

work page internal anchor Pith review arXiv 2021
[2]

Data centers on wheels: Emissions from computing on- board autonomous vehicles.IEEE Micro, 43(1):29–39,

Soumya Sudhakar, Vivienne Sze, and Sertac Karaman. Data centers on wheels: Emissions from computing on- board autonomous vehicles.IEEE Micro, 43(1):29–39,
[3]

doi: 10.1109/MM.2022.3219803

work page doi:10.1109/mm.2022.3219803 2022
[4]

Integer factorization using stochastic magnetic tunnel junctions.Nature, 573(7774):390–393, 2019

William A Borders, Ahmed Z Pervaiz, Shunsuke Fukami, Kerem Y Camsari, Hideo Ohno, and Supriyo Datta. Integer factorization using stochastic magnetic tunnel junctions.Nature, 573(7774):390–393, 2019

2019
[5]

Shuvro Chowdhury, Andrea Grimaldi, Navid Anjum Aa- dit, Shaila Niazi, Masoud Mohseni, Shun Kanai, Hideo Ohno, Shunsuke Fukami, Luke Theogarajan, Giovanni Finocchio, et al. A full-stack view of probabilistic computing with p-bits: Devices, architectures, and al- gorithms.IEEE Journal on Exploratory Solid-State Computational Devices and Circuits, 9(1):1–11, 2023

2023
[6]

From mtj device to hybrid cmos/mtj circuits: A review.IEEE Access, 8:194105– 194146, 2020

Vinod Kumar Joshi, Prashanth Barla, Somashekara Bhat, and Brajesh Kumar Kaushik. From mtj device to hybrid cmos/mtj circuits: A review.IEEE Access, 8:194105– 194146, 2020. doi: 10.1109/ACCESS.2020.3033023

work page doi:10.1109/access.2020.3033023 2020
[7]

Stochastic analysis of retention time of coupled memory topology,

Anirudh Bangalore Shankar, Avhishek Chatterjee, Bhaswar Chakrabarti, and Anjan Chakravorty. Stochastic analysis of retention time of coupled memory topology,
[8]

URL https://arxiv.org/abs/2412.13197

work page arXiv
[9]

Towards an energy con- sumption index for deep learning models: A comparative analysis of architectures, gpus, and measurement tools

Sergio Aquino-Br ´ıtez, Pablo Garc´ıa-S´anchez, Andr´es Or- tiz, and Diego Aquino-Br ´ıtez. Towards an energy con- sumption index for deep learning models: A comparative analysis of architectures, gpus, and measurement tools. Sensors, 25(3):846, 2025

2025
[10]

mlco2/codecarbon: v2.4.1, May

Benoit Courty et al. mlco2/codecarbon: v2.4.1, May
[11]

URL https://doi.org/10.5281/zenodo.11171501

work page doi:10.5281/zenodo.11171501
[12]

arXiv preprint arXiv:2007.03051 (2020), https://arxiv.org/abs/2007.03051

Lasse F. Wolff Anthony, Benjamin Kanding, and Raghavendra Selvan. Carbontracker: Tracking and pre- dicting the carbon footprint of training deep learning models.ArXiv, abs/2007.03051, 2020. URL https: //api.semanticscholar.org/CorpusID:220381235

work page arXiv 2007
[13]

doi: 10.1002/advs.202100707

Lo ¨ıc Lannelongue, Jason Grealey, and Michael Inouye. Green algorithms: Quantifying the carbon footprint of computation.Advanced Science, 8(12):2100707, 2021. doi: https://doi.org/10.1002/advs.202100707. URL https://advanced.onlinelibrary.wiley.com/doi/abs/10.10 02/advs.202100707

work page doi:10.1002/advs.202100707 2021
[14]

Hardware architecture and soft- ware stack for pim based on commercial dram tech- nology : Industrial product

Jamie Liu, Ben Jaiyen, Richard Veras, and Onur Mutlu. Raidr: Retention-aware intelligent dram refresh. In2012 39th Annual International Symposium on Computer Ar- chitecture (ISCA), pages 1–12, 2012. doi: 10.1109/ISCA .2012.6237001

work page doi:10.1109/isca 2012
[15]

Magnetic and magnetic inverse spin hall effects in a non-collinear antiferromagnet.Nature, 565(7741):627–630, 2019

Motoi Kimata, Hua Chen, Kouta Kondou, Satoshi Sugi- moto, Prasanta K Muduli, Muhammad Ikhlas, Yasutomo Omori, Takahiro Tomita, Allan H MacDonald, Satoru Nakatsuji, et al. Magnetic and magnetic inverse spin hall effects in a non-collinear antiferromagnet.Nature, 565(7741):627–630, 2019

2019
[16]

Anoma- lous hall effect arising from noncollinear antiferromag- netism.Physical review letters, 112(1):017205, 2014

Hua Chen, Qian Niu, and Allan H MacDonald. Anoma- lous hall effect arising from noncollinear antiferromag- netism.Physical review letters, 112(1):017205, 2014

2014
[17]

New heights for hard disk drives

Jian-Gang Jimmy Zhu. New heights for hard disk drives. Materials Today, 6(7-8):22–31, 2003

2003
[18]

Stress-induced magnetic anisotropy in fe-based amor- phous/nanocrystalline alloys: Mechanisms, advances and challenges.Materials, 18(7):1499, 2025

Jianqiang Zhang, Yanjun Qin, Xiaobin Liu, Yuxiang Zhao, Wenqiang Dang, Xiaozhen Fan, Xinyi Chen, Yuanrong Yu, Zixuan Yang, Shipeng Gao, et al. Stress-induced magnetic anisotropy in fe-based amor- phous/nanocrystalline alloys: Mechanisms, advances and challenges.Materials, 18(7):1499, 2025

2025
[19]

Cam- bridge University Press, 2023

David J Griffiths.Introduction to electrodynamics. Cam- bridge University Press, 2023

2023
[20]

Magnetic dipole-dipole interaction induced by the electromagnetic field.Physical Review A, 97(1):013819, 2018

Jiaxuan Wang, Hui Dong, and Sheng-Wen Li. Magnetic dipole-dipole interaction induced by the electromagnetic field.Physical Review A, 97(1):013819, 2018

2018
[21]

Ma- terials that couple sensing, actuation, computation, and communication.Science, 347(6228):1261689, 2015

Michael Andrew McEvoy and Nikolaus Correll. Ma- terials that couple sensing, actuation, computation, and communication.Science, 347(6228):1261689, 2015

2015
[22]

Probabilistic neural computing with stochastic devices.Advanced Materials, 35(37):2204569, 2023

Shashank Misra, Leslie C Bland, Suma G Cardwell, Jean Anne C Incorvia, Conrad D James, Andrew D Kent, Catherine D Schuman, J Darby Smith, and James B Aimone. Probabilistic neural computing with stochastic devices.Advanced Materials, 35(37):2204569, 2023

2023
[23]

Refresh algorithm for ensuring 100IEEE Access, 9:105831–105840, 2021

Roman Golman, Netanel Nachum, Tomer Cohen, Robert Giterman, and Adam Teman. Refresh algorithm for ensuring 100IEEE Access, 9:105831–105840, 2021. doi: 10.1109/ACCESS.2021.3099970

work page doi:10.1109/access.2021.3099970 2021
[24]

Renewal theory and its ramifications

Walter L Smith. Renewal theory and its ramifications. Journal of the Royal Statistical Society Series B: Statis- tical Methodology, 20(2):243–284, 1958

1958