Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/7820
Title: Energy-aware assignment and scheduling for hybrid main memory in embedded systems
Authors: Wang, G
Guan, Y
Wang, Y
Shao, Z 
Keywords: Embedded systems
Energy consumption
Hybrid memory architecture
PCM (phase change memory)
Issue Date: 2015
Publisher: Springer
Source: Computing, 2015 How to cite?
Journal: Computing 
Abstract: In embedded systems, especially battery-driven mobile devices, energy is one of the most critical performance metrics. Due to its high density and low standby power, phase change memory (PCM), an emerging non-volatile memory device, is becoming a promising dynamic random access memory (DRAM) alternative. Recent studies have proposed the hybrid main memory architecture integrating both PCM and DRAM to fully take advantage of the properties of both memories. However, the low power performance of PCM in the hybrid main memory architecture has not been fully explored. Therefore, it becomes an interesting problem to utilize PCM and DRAM as hybrid main memory for energy optimization in embedded systems. In this paper, we present an energy optimization technique for hybrid main memory architecture. The objective is to fully utilize PCM to reduce the energy consumption while ensuring that the real-time performance of applications are guaranteed. We propose a two-phase approach to solve hybrid main memory address mapping problem. In the first phase, we calculate energy and time cost for each address based on the task models. Then the applications can be modeled as data-flow graph nodes, and different access times will associate with different energy consumption. In the second phase, for different memory types and the given timing constraint, we formulate the scheduling problem as an integer linear programming (ILP) model and obtain an optimal solution. The ILP model can map a proper memory type for each address such that the total energy consumption can be minimized while the timing constraint is satisfied. In addition, we propose a heuristic approach to efficiently obtain a near-optimal solution. We conduct experiments on an ARM-based simulator. The experimental results show that our method can effectively reduce the energy consumption with the least system cost compared with the previous work.
URI: http://hdl.handle.net/10397/7820
ISSN: 0010-485X
EISSN: 1436-5057
DOI: 10.1007/s00607-015-0464-7
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