TY - GEN
T1 - From "mISSION
T2 - 50th IEEE International Symposium on Circuits and Systems, ISCAS 2017
AU - Li, Qin
AU - Liu, Zheyu
AU - Qiao, Fei
AU - Wu, Xing
AU - Wang, Chaolun
AU - Wei, Qi
AU - Yang, Huazhong
N1 - Publisher Copyright:
© 2017 IEEE.
PY - 2017/9/25
Y1 - 2017/9/25
N2 - 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.
AB - 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.
KW - Analog-to-information processing
KW - Contact lens
KW - Flexible-device
KW - MLP
UR - https://www.scopus.com/pages/publications/85032696899
U2 - 10.1109/ISCAS.2017.8050607
DO - 10.1109/ISCAS.2017.8050607
M3 - 会议稿件
AN - SCOPUS:85032696899
T3 - Proceedings - IEEE International Symposium on Circuits and Systems
BT - IEEE International Symposium on Circuits and Systems
PB - Institute of Electrical and Electronics Engineers Inc.
Y2 - 28 May 2017 through 31 May 2017
ER -
