A fully convolutional network and an example of activation function multiplexing. (IMAGE)

Intelligent Computing

A fully convolutional network and an example of activation function multiplexing.

Caption

(A) A traditional fully connected neural network. The layers are connected by black lines corresponding to weights. The neurons separately realize the summation and nonlinear activation functions without influencing others. (B) An example of a nonlinear activation function and how it can be conceptually multiplexed in a single device.

Credit

Yi-Feng Liu et al.

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License

CC BY

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