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We present an interpretation of Inception modules in convolutional neural networks as being an intermediate step in-between regular convolution and the depthwise separable convolution operation (a depthwise convolution followed by a pointwise convolution). In this light, a depthwise separable convolution can be understood as an Inception module with a maximally large number of towers. This observation...
Clique-based neural networks are less complex than commonly used neural network models. They have a limited connectivity and are composed of simple functions. They are thus adapted to implement neuro-inspired computation units operating under severe energy constraints. This paper shows an ST 65-nm CMOS ASIC implementation for a 30-neuron clique-based neural network circuit. With a 1V power supply...
To operate under severe energy constraints, clique-based neural networks are good candidates. They benefit from a reduced exchange of information between low-complexity processing units with no performance degradation. This paper proposes a modular, flexible and scalable architecture validated by an ST 65-nm CMOS ASIC implementation for a 30-neuron clique-based neural network circuit. With 0.8V power...
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