Lately, more and more applications are deployed on heterogeneous, power-constrained edge-computing devices. Bringing computation closer to the data, contributes both to latency and energy consumption reduction due to the elimination of excessive data transfers. However, while the main concern in such environments is the minimization of energy consumption, the heterogeneity in compute resources found at the edge may lead to Quality of Service (QoS) violations. At the same time, Serverless computing, the next frontier of Cloud computing has emerged to offer unprecedented elasticity by utilizing fine-grained, stateless functions. The reduction in the execution time and the modest memory footprint of such decomposed applications, allow for fine-grained resource multiplexing. In this work, we propose a methodology for application decomposition into fine-grained functions and energy-aware function placement on a cluster of edge devices subject to user-specified QoS guarantees.