Investigating novel techniques to improve many-core embedded systems lifetime, reliability, and thermal management is a fundamental challenge for the semiconductor industry. Imbalanced mapping of applications may considerably affect the system performance and lifetime due to thermal issues in an integrated circuit (e.g. hotspot zones). Traditional mapping techniques focus on local optimizations, e.g. minimize the number of hops between communicating tasks, which may lead to hotspot zones and underutilization of some processing resources. This paper proposes a runtime mapping heuristic whose cost function targets temporal workload and energy consumption balance in large scale systems. The proposed heuristic minimizes the occurrence of hotspots by distributing application workload onto the processing elements in a uniform way, which contributes to a balanced thermal distribution across the system. These features improve system reliability and postpone aging effects. Results with several benchmarks executing in a cycle-accurate platform model show a uniform system utilization when comparing the proposed heuristic to conventional mapping approaches.