Heterogeneous Dual-Core Overlay Processor for Light-Weight CNNs

Convolutional neural networks (CNNs) have achieved extensive success on miscellaneous artificial intelligence applications such as image classification and object detection. A plethora of models emerge with different operators and architectures, gradually shifting attention from accuracy to efficiency in terms of speed and power, since VGG-like architecture from early stage has significant redundancy. Light-weight CNNs are proposed to reduce computation complexity and parameter amount. MobileNets, one typical example of light-weight CNNs, adopt depthwise separable convolution, while others such as SqueezeNet alter model topology to spare computation power.