  Synapse crossbar is an elementary structure in Neuromorphic Computing Systems
(NCS). However, the limited size of crossbars and heavy routing congestion
impedes the NCS implementations of big neural networks. In this paper, we
propose a two-step framework (namely, group scissor) to scale NCS designs to
big neural networks. The first step is rank clipping, which integrates low-rank
approximation into the training to reduce total crossbar area. The second step
is group connection deletion, which structurally prunes connections to reduce
routing congestion between crossbars. Tested on convolutional neural networks
of LeNet on MNIST database and ConvNet on CIFAR-10 database, our experiments
show significant reduction of crossbar area and routing area in NCS designs.
Without accuracy loss, rank clipping reduces total crossbar area to 13.62\% and
51.81\% in the NCS designs of LeNet and ConvNet, respectively. Following rank
clipping, group connection deletion further reduces the routing area of LeNet
and ConvNet to 8.1\% and 52.06\%, respectively.
