Recently, the application of machine learning on embedded systems has drawn interest in both the research community and industry because embedded systems located at the edge can produce a faster response and reduce network load. However, software implementation of neural networks on Central Processing Units (CPUs) is considered infeasible in embedded systems due to limited power supply. To accelerate AI processing, the many-core Graphics Processing Unit (GPU) has been a preferred device to the CPU. However, its energy efficiency is not still considered to be good enough for embedded systems. Among other approaches for machine learning on embedded systems, neuromorphic processing chips are expected to be less power-consuming and overcome the memory bottleneck. In this work, we implemented a pedestrian image detection system on an embedded device using a commercially available neuromorphic chip, NM500, which is based on NeuroMem technology. The NM500 processing time and the power consumption were measured as the number of chips was increased from one to seven, and they were compared to those of a multicore CPU system and a GPU-accelerated embedded system. The results show that NM500 is more efficient in terms of energy required to process data for both learning and classification than the GPU-accelerated system or the multicore CPU system. Additionally, limits and possible improvement of the current NM500 are identified based on the experimental results.
In this study, we have developed an image transforming optical element which realized a zero-bezel flat panel display module. The zero-bezel display module worked not only at the front view but also did at the oblique view, so that omnidirectional image expression was achieved. In addition, resolution of the display was not degraded after addition of the optical element because the resolution of the image transformation element was higher enough to deliver the image with the original resolution. This achievement can be used for a modular unit of a seamless multivision display.
In this paper, we propose a power management framework for solar-powered Internet of Things (IoT) devices to maximize the lifetime of the system by adaptively changing performance of the system. Our framework balances the energy consumption and the performance of the IoT system by monitoring energy accumulation and battery, and ensures the minimum level of service. The framework has been implemented in real hardware and software. Experiments with the implemented system shows that our framework can help increase the lifetime of the solar powered IoT system. The framework prevented the IoT device from power down due to the full discharge of the battery at night time, by changing the execution rate based on energy accumulation and battery status. The proposed framework has been implemented using open hardware compatible with Arduino, so it can be used for a wide range of IoT applications and services.
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