Energy Transparency for Deeply Embedded Programs

TL;DR

This paper introduces a novel method for enabling energy transparency in deeply embedded IoT devices by estimating energy consumption at the LLVM IR level without hardware measurements, aiding energy-aware optimization.

Contribution

It presents a new mapping technique and profiling method that provide accurate energy estimations at the LLVM IR level, integrating energy transparency into the LLVM optimizer.

Findings

High accuracy in energy estimation with 1% deviation using SRA.

Profiling technique achieves an average error of 3%.

Applicable to multi-threaded embedded benchmarks.

Abstract

Energy transparency is a concept that makes a program's energy consumption visible, from hardware up to software, through the different system layers. Such transparency can enable energy optimizations at each layer and between layers, and help both programmers and operating systems make energy-aware decisions. In this paper, we focus on deeply embedded devices, typically used for Internet of Things (IoT) applications, and demonstrate how to enable energy transparency through existing Static Resource Analysis (SRA) techniques and a new target-agnostic profiling technique, without hardware energy measurements. Our novel mapping technique enables software energy consumption estimations at a higher level than the Instruction Set Architecture (ISA), namely the LLVM Intermediate Representation (IR) level, and therefore introduces energy transparency directly to the LLVM optimizer. We apply our energy estimation techniques to a comprehensive set of benchmarks, including single- and also multi-threaded embedded programs from two commonly used concurrency patterns, task farms and pipelines. Using SRA, our LLVM IR results demonstrate a high accuracy with a deviation in the range of 1% from the ISA SRA. Our profiling technique captures the actual energy consumption at the LLVM IR level with an average error of 3%.