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From 'MISSION: IMPOSSIBLE' to mission possible: Fully flexible intelligent contact lens for image classification with analog-to-information processing

机译:从“使命:不可能”到任务可能:具有模拟到信息处理的图像分类的完全灵活的智能隐形眼镜

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A prototype of fully flexible intelligent contact lens, which are shown in the impressive action movie series of "MISSION: IMPOSSIBLE", has become the Possible Mission in this work. Hereon, the system adopts analog-to-information processing method to build a specific Multi-Layer Perceptron network for image classification tasks with flexible devices and circuits, where the information is extracted from raw data of the sensing analog signal directly. Simulated with HSPICE of Level-62 TFT device model, for standard test image data set of MNIST, the classification accuracy of the presented flexible neural network circuit is up to 92.99%; meanwhile, the classification speed is as fast as 10k fps, and the energy consumption is low to only 15.16μJ. Additionally, for the imperfections of flexible devices of larger devices mismatch and process variations, the fault-tolerance of the system has been evaluated as well, which demonstrates the feasibility of the presented methods and lowers the barrier to integrated all kinds of FLEXIBLE Devices into a FULLY FLEXIBLE Systems with sensors, processing parts and even energy harvesting parts, etc., in the future wearable smart terminals.
机译:完全灵活的智能隐形眼镜的原型,令人印象深刻的动作电影系列“使命:不可能”,已成为这项工作中可能的使命。在此,系统采用模数到信息处理方法来构建具有灵活设备和电路的图像分类任务的特定多层的Perceptron网络,其中信息被直接从感测模拟信号的原始数据提取。用Level-62 TFT器件模型的HSPICE模拟,对于MNIST的标准测试图像数据集,所呈现的柔性神经网络电路的分类精度高达92.99%;同时,分类速度与10K FPS快,能量消耗低至仅为15.16μj。另外,对于较大器件的柔性器件的缺陷不匹配和工艺变化,还评估了系统的容错,这表明了所提出的方法的可行性,并将屏障降低到将各种柔性器件集成到A中的屏障在未来的可穿戴智能终端中,具有传感器,加工部件甚至能量收集部件等的完全灵活的系统。

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