Energy consumption has been a big challenge for electronic devices, particularly, for the battery-powered Internet of Things (IoT) equipment. To address such a challenge, on 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 the energy harvesting technology. In this paper, 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 32MB STT-MRAM, and experimentally demonstrated the system functionality and efficiency under five typical energy harvesting scenarios, including radio-frequency (RF), 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.
There is an ongoing trend to increasingly offload inference tasks, such as CNNs, to edge devices in many IoT scenarios. As energy harvesting is an attractive IoT power source, recent ReRAM-based CNN accelerators have been designed for operation on harvested energy. When addressing the instability problems of harvested energy, prior optimization techniques often assume that the load is fixed, overlooking the close interactions among input power, computational load, and circuit efficiency, or adapt the dynamic load to match the just-in-time incoming power under a simple harvesting architecture with no intermediate energy storage. Targeting a more efficient harvesting architecture equipped with both energy storage and energy delivery modules, this paper is the first effort to target whole system, end-to-end efficiency for an energy harvesting ReRAM-based accelerator. First, we model the relationships among ReRAM load power, DC-DC converter efficiency, and power failure overhead. Then, a maximum computation progress tracking scheme ( MaxTracker ) is proposed to achieve a joint optimization of the whole system by tuning the load power of the ReRAM-based accelerator. Specifically, MaxTracker accommodates both continuous and intermittent computing schemes and provides dynamic ReRAM load according to harvesting scenarios. We evaluate MaxTracker over four input power scenarios, and the experimental results show average speedups of 38.4%/40.3% (up to 51.3%/84.4%), over a full activation scheme (with energy storage) and order-of-magnitude speedups over the recently proposed (energy storage-less) ResiRCA technique. Furthermore, we also explore MaxTracker in combination with the Capybara reconfigurable capacitor approach to offer more flexible tuners and thus further boost the system performance.
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