Most modern multi-core edge devices work in outdoor situations with limited power supplies like energy harvester and batteries. Therefore, energy consumption is a fundamental issue in which the memory subsystem has a significant role. Scratchpad memories (SPM) can provide a broad potential for energy saving. Still, due to the insufficient SPM capacity in such edge devices, a rigorous SPM data allocation scheme is necessary to reduce the energy consumption of the memory subsystem. Emerging non-volatile memories (NVMs) are very useful to reduce the energy consumption of the memory subsystem. Compared with SRAM, NVMs have lower leakage power and higher density, but the read and write latencies of the NVMs are higher than the SRAM. Therefore, embedded and edge devices can take advantage of hybrid SPM composed of both NVM and SRAM to achieve further energy saving. This paper proposes MASTER, a task mapping, task scheduling, and dynamic SPM allocation scheme that efficiently utilizes the hybrid SPM space. To this end, we model the hybrid SPM allocation on a multi-core system with integer linear programming formulation to minimize the energy consumption of the memory subsystem. Experimental results show that MASTER improves the energy saving of the memory subsystem by up to 34 percent compared to EADA, which is a heuristic dynamic data allocation algorithm for multi-core systems with hybrid SPM.