Machine intelligence has been improving significantly with explosive interest in deep learning. Machines are now a match for humans at the functionality in some specific applications, but the energy-efficiency of today’s computing platforms running deep learning models is not comparable to that of the human brain. Neuromorphic hardware has drawn attention as an approach to deal with the issues of today’s computing platforms based on Von Neumann architecture when running deep learning models, but large-scale deep neural networks such as AlexNet have not been demonstrated yet in any neuromorphic systems. Since 2014, we have been developing a non-Von Neumann computing system called INSight based on data flow architecture that aims at running large-scale deep neural networks in the neuromorphic fashion. We have now reached a major milestone and will demonstrate INSight running the convolutional layers of AlexNet. The proposed system is implemented with Xilinx Virtex 7 FPGA and performs the processing using 100K synapses mapped on LUTs without any array-type memories. It processes 1552 images per second and consumes 7.2W, resulting in the state-of-the-art energy efficiency on FPGA.