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Jeff Dix, Jeremy Holleman and Benjamin J. Blalock
A programmable, energy-efficient analog hardware implementation of a multilayer perceptron (MLP) is presented featuring a highly programmable system that offers the user the capability to create an MLP neural network hardware design within the available ...
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Andrea Di Salvo, Sara Garbolino, Marco Mignone, Stefan Cristi Zugravel, Angelo Rivetti, Mario Edoardo Bertaina and Pietro Antonio Palmieri
This work presents the development of a 64-channel application-specific integrated circuit (ASIC), implemented to detect the optical Cherenkov light from sub-orbital and orbital altitudes. These kinds of signals are generated by ultra-high energy cosmic ...
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Zuoqin Zhao, Yufei Nai, Zhiguo Yu, Xin Xu, Xiaoyang Cao and Xiaofeng Gu
Compressed Sensing (CS) has been applied to electrocardiogram monitoring in wireless sensor networks, but existing sampling and compression circuits consume too much hardware. This paper proposes a low-power and small-area sampling and compression circui...
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Yunfeng Hu, Bin Tang, Chaoyi Chen, Lexing Hu, Qingming Huang, Jiaqi Cai, Jinbo Xie, Bin Li and Zhaohui Wu
In recent years, due to the rise of the Internet of Things (IoT), various sensors have come to be in great demand for IoT devices. Analog-to-digital converters (ADCs) act as an important part of receivers in sensors. To improve the uptime of IoT devices,...
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Taylor Barton, Hao Yu, Kyle Rogers, Nancy Fulda, Shiuh-hua Wood Chiang, Jordan Yorgason and Karl F. Warnick
We present a transfer learning method inspired by modulatory neurotransmitter mechanisms in biological brains and explore applications for neuromorphic hardware. In this method, the pre-trained weights of an artificial neural network are held constant an...
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