This paper presents temperature aware low power scheduling under resource and latency constraints. We assume resources with different energy delay values are available. These resources are optimized in terms of energy for a certain delay, using variable supply voltage, multiple threshold voltages and sizing techniques. The proposed algorithms are based on temperature and power efficient distribution of slack among the nodes in the data-flow graph. The distribution procedure tries to implement the minimum energy scheduling when there is no temperature critical points. If a functional unit reaches a critical temperature, algorithm tries not to schedule any nodes in the data flow graph to high temperature resources, thus decrease the chip temperature. Experiments with some HLS benchmark examples show that the proposed algorithms achieve significant power/energy reduction. For instance, when the latency constraint is 2 times the critical path delay and one of the resource temperature is critical the average power reduction is 50.8% and utilization of the hot resource is average 1%.