  Hardware accelerator for convolution neural network (CNNs) enables real time
applications of artificial intelligence technology. However, most of the
accelerators only support dense CNN computations or suffers complex control to
support fine grained sparse networks. To solve above problem, this paper
presents an efficient CNN accelerator with 1-D vector broadcasted input to
support both dense network as well as vector sparse network with the same
hardware and low overhead. The presented design achieves 1.93X speedup over the
dense CNN computations.
