R&D: Experimental Demo of STT-MRAM-Based Nonvolatile Instantly On/Off System for IoT Applications, Case Studies

ACM Transactions on Embedded Computing Systems has published an article written by Yueting Li, Wang Kang, Beihang University, Beijing, China, Kunyu Zhou, Keni Qiu, Capital Normal University, Beijing, China, and Weisheng Zhao, Beihang University, Beijing, China.

Abstract: “Energy consumption has been a big challenge for electronic devices, particularly for battery-powered Internet of Things (IoT) equipment. To address such a challenge, on the one hand, low-power electronic design methodologies and novel power management techniques have been proposed, such as nonvolatile memories and instantly on/off systems; on the other hand, the energy harvesting technology by collecting signals from human activity or the environment has attracted widespread attention in the IoT area. However, the system with self-powered energy harvesting may suffer frequent energy failures or fluctuating energy conditions, which degrade system reliability and user experience. Therefore, how to make the system under unreliable power inputs operate correctly and efficiently is one of the most critical issues for energy harvesting technology. In this article, we built an instantly on/off system based on nonvolatile STT-MRAM for IoT applications, which can instantly power on/off under different conditions of the harvested energy. The system powers on and operates normally when the harvested energy is enough (over the preset threshold); otherwise, the system powers off and stores the operational data back to the nonvolatile STT-MRAM. We described implementations of the hardware/software co-designed architecture (with image acquisition as an example) based on the commercialized 32 MB STT-MRAM, and we experimentally demonstrated the system functionality and efficiency under five typical energy harvesting scenarios, including radio frequency, thermal, solar, piezoelectric, and WIFI. Our experimental results show that the power consumption and data restore time were reduced by 15.1% and 714 times, respectively, in comparison with the DRAM-based counterpart.“