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Sensor nodes are used extensively in order to gather real-time information in the social environment and natural environment. And the production volume of sensor nodes is much increased with the development of cyber-physical systems. Therefore, it becomes important how to reduce the power consumption of huge sensor nodes. In this work, normally-off architecture of microcontroller for future low-power sensor node is proposed. To realize true low-power effects with normally-off computing technology, a co-design of hardware and software technology is much important. In this work, the power consumption of sensor nodes is possible to reduce of around 70% by using normally-off MCU architecture in sensor node.

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Shifting to multi-core designs is so pervasive a trend to overcome the power wall and it is a necessary move for embedded systems in our rapidly evolving information society. Meanwhile, the need to increase the battery life and reduce maintenance costs for such embedded systems is very critical. Therefore, a wide variety of power reduction techniques have been proposed and realized, including Clock Gating, DVFS and Power Gating. To maximize the effectiveness of these techniques, task scheduling is a key but for multi-core systems it is very complicated due to the huge exploration space. This problem is a major obstacle for further power reduction. To cope with it, we propose a design method for embedded systems to minimize their energy consumption under performance constraints. This method is based on the clarification of properties of the above mentioned low power techniques and their interactions. In more details, we firstly establish energy models for these low power techniques and our target systems. We then explore for the best configuration by constructing an optimization problem especially for applications which have a longer deadline than the execution interval. Finally, we propose an approximate solution using dynamic programming with a lower computation complexity and compare it to a brute force explicit solution. We confirm with our evaluations that the proposed method successfully found a better configuration which reduces the total energy consumption by 32% if compared to the manually optimized configuration, which utilizes only one core.

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