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• W2078487277 abstract "This paper studies the application of tunnel FET (TFET) in designing a low power and robust cellular neural network (CNN)-based associative memory (AM). The lower leakage, steeper switching slope, and higher output resistance of TFET are exploited in designing an ultralow-power TFET-based operational transconductance amplifier (OTA). A TFET-OTA is utilized as a programmable synaptic weight multiplier for CNN. The ultralow-power of TFET-OTA enables a higher connectivity network even at a lower power, and thereby improves the memory capacity and input pattern noise tolerance of CNN-AM for low power applications. The TFET-based higher connectivity CNN also exploits the unique characteristics of TFET to improve the throughput efficiency of CNN-AM." @default.
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• W2078487277 date "2014-11-01" @default.
• W2078487277 modified "2023-09-25" @default.
• W2078487277 title "Application of Silicon-Germanium Source Tunnel-FET to Enable Ultralow Power Cellular Neural Network-Based Associative Memory" @default.
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• W2078487277 doi "https://doi.org/10.1109/ted.2014.2357777" @default.
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