A streaming accelerator of Convolutional Neural Networks for resource-limited applications

Convolutional Neuronal Networks (CNN) implementation on embedded devices is restricted due to the number of layers of some CNN models. In this context, this paper describes a novel architecture based on Layer Operation Chaining (LOC) which uses fewer convolvers than convolution layers. A reutilization of hardware convolvers is promoted through kernel decomposition. Thus, an architectural design with reduced resources utilization is achieved, suitable to be implemented on low-end devices as a solution for portable classification applications. Experimental results show that the proposed design has a competitive processing time and overcomes resource utilization when compared with state-of-the-art related works.