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Bibliographic Details
Main Author: Perrier, Elija
Format: Preprint
Published: 2025
Subjects:
Online Access:https://arxiv.org/abs/2504.05328
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author Perrier, Elija
author_facet Perrier, Elija
contents We present a mathematical framework for quantifying energy efficiency in intelligent systems by linking energy consumption to information-processing capacity. We introduce a watts-per-intelligence metric that integrates algorithmic thermodynamic principles of Landauer with computational models of machine intelligence. By formalising the irreversible energy costs of computation, we derive rigorous lower bounds on energy usage of algorithmic intelligent systems and their adaptability. We introduce theorems that constrain the trade offs between intelligence output and energy expenditure. Our results contribute to design principles for energy-efficient intelligent systems.
format Preprint
id arxiv_https___arxiv_org_abs_2504_05328
institution arXiv
publishDate 2025
record_format arxiv
spellingShingle Watts-Per-Intelligence: Part I (Energy Efficiency)
Perrier, Elija
Information Theory
We present a mathematical framework for quantifying energy efficiency in intelligent systems by linking energy consumption to information-processing capacity. We introduce a watts-per-intelligence metric that integrates algorithmic thermodynamic principles of Landauer with computational models of machine intelligence. By formalising the irreversible energy costs of computation, we derive rigorous lower bounds on energy usage of algorithmic intelligent systems and their adaptability. We introduce theorems that constrain the trade offs between intelligence output and energy expenditure. Our results contribute to design principles for energy-efficient intelligent systems.
title Watts-Per-Intelligence: Part I (Energy Efficiency)
topic Information Theory
url https://arxiv.org/abs/2504.05328