A Probability-based Data Allocation Strategy for Hybrid DRAMINVM Memory in Real-time Embedded Systems

Non-volatile memory (NVM) has emerged as a promlsmg DRAM alternative due to its high density, and zero leakage power. Nevertheless, it suffers from higher write energy. According to a given data-access frequencies, the previous studies focus on data allocation technique for utilizing the benefits of both NVM and DRAM. However, data-access frequencies is often obtained with probability, which could not be effectively applied on the previous techniques. To address this issue, this paper proposes a probability-based data allocation strategy for hybrid DRAMINVM memory in real-time embedded systems, The basic idea is first to obtain the probability-based data-access frequencies of a given embedded program by exploiting its appllcatlon-speciflc feature. Combining with the maximum data access frequencies and the data-access frequency expectations of each data in a given program, this paper proposes a novel and simple data allocation algorithm, named PBDA, to minimize the energy consumption of real-time embedded system, Finally, compared to the Greedy algorithm and an existing optimal data allocation algorithm, the experiments show that our technology can reduce energy consumption by 45.01% and 10.49% on average.