Low Precision and Efficient Programming Languages for Sustainable AI: Final Report for the Summer Project of 2024

Joao Vitor De Oliveira Silva, Tokey Tahmid, Weslley da Silva Pereira

Research output: NRELTechnical Report

Abstract

This document contains all relevant material generated during the authors' summer internship at NREL in 2024. This report shows how to improve energy efficiency of a few code samples by using low-precision data types combined with mixed-precision algorithms. The main applications considered here are (i) linear system solvers using mixed precision, and (ii) neural networks using mixed precision. This report also discusses how programming languages affect energy consumption of algorithms, energy metrics for a code and tools, and the available current software and hardware infrastructure.
Original languageAmerican English
Number of pages27
DOIs
StatePublished - 2024

NREL Publication Number

  • NREL/TP-2C00-90910

Keywords

  • energy efficiency
  • linear system solver
  • low precision
  • mixed precision
  • neural networks
  • sustainability

Fingerprint

Dive into the research topics of 'Low Precision and Efficient Programming Languages for Sustainable AI: Final Report for the Summer Project of 2024'. Together they form a unique fingerprint.

Cite this