# Thermodynamic cost and benefit of memory

## TL;DR

This paper explores the fundamental thermodynamic limits of information processing, showing that predictive inference and data compression strategies optimize energy efficiency by minimizing dissipation in information engines.

## Contribution

It derives a generalized lower bound on dissipation for partially observable information engines and links predictive inference to thermodynamic efficiency.

## Key findings

- Retention of irrelevant information limits efficiency
- Data compression based on relevance optimizes dissipation
- Predictive inference aligns with thermodynamic efficiency

## Abstract

This letter exposes a tight connection between the thermodynamic efficiency of information processing and predictive inference. A generalized lower bound on dissipation is derived for partially observable information engines which are allowed to use temperature differences. It is shown that the retention of irrelevant information limits efficiency. A data representation strategy is derived from optimizing a fundamental physical limit to information processing: minimizing the lower bound on dissipation leads to a data compression method that maximally retains relevant, predictive, information. In that sense, predictive inference emerges as the strategy that least precludes energy efficiency.