  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%.
