Trading Computation for Communication: A Taxonomy

TL;DR

This paper presents a taxonomy and quantitative analysis of trading computation for communication to enhance energy efficiency in system design, addressing the high costs of data movement versus computation.

Contribution

It introduces a comprehensive taxonomy and quantitative framework for understanding computation-communication trade-offs in modern systems.

Findings

Provides a structured taxonomy of computation vs. communication trade-offs.

Offers quantitative characterization of the trade-offs.

Highlights potential energy savings through computation-communication balancing.

Abstract

A critical challenge for modern system design is meeting the overwhelming performance, storage, and communication bandwidth demand of emerging applications within a tightly bound power budget. As both the time and power, hence the energy, spent in data communication by far exceeds the energy spent in actual data generation (i.e., computation), (re)computing data can easily become cheaper than storing and retrieving (pre)computed data. Therefore, trading computation for communication can improve energy efficiency by minimizing the energy overhead incurred by data storage, retrieval, and communication. This paper hence provides a taxonomy for the computation vs. communication trade-off along with quantitative characterization.