Energy-Efficient Algorithms

TL;DR

This paper introduces a theoretical framework for analyzing and designing energy-efficient algorithms based on physical principles, extending traditional models to include energy complexity and demonstrating low-energy variants of classic algorithms.

Contribution

It proposes energy-aware computational models and develops low-energy versions of standard algorithms, establishing a foundation for semi-reversible computing and energy-efficient algorithm design.

Findings

Energy-aware models with zero energy complexity primitives

Low-energy variants of classic algorithms like sorting and graph algorithms

Analysis of time, space, and energy trade-offs in algorithms

Abstract

We initiate the systematic study of the energy complexity of algorithms (in addition to time and space complexity) based on Landauer's Principle in physics, which gives a lower bound on the amount of energy a system must dissipate if it destroys information. We propose energy-aware variations of three standard models of computation: circuit RAM, word RAM, and transdichotomous RAM. On top of these models, we build familiar high-level primitives such as control logic, memory allocation, and garbage collection with zero energy complexity and only constant-factor overheads in space and time complexity, enabling simple expression of energy-efficient algorithms. We analyze several classic algorithms in our models and develop low-energy variations: comparison sort, insertion sort, counting sort, breadth-first search, Bellman-Ford, Floyd-Warshall, matrix all-pairs shortest paths, AVL trees, binary heaps, and dynamic arrays. We explore the time/space/energy trade-off and develop several general techniques for analyzing algorithms and reducing their energy complexity. These results lay a theoretical foundation for a new field of semi-reversible computing and provide a new framework for the investigation of algorithms.