SP-CNN: A Scalable and Programmable CNN-Based Accelerator

Dilan Manatunga, Hyesoon Kim, Saibal Mukhopadhyay

Abstract: Specialized accelerators have become prevalent in many mobile computing platforms for their ability to perform certain tasks, such as image processing, at a lower power cost than a generalized CPU or GPU. In this article, the authors focus on using cellular neural networks (CNNs) as a specialized accelerator. CNN is a neural computing paradigm that is well suited for image processing applications. However, hardware implementations were originally developed to handle only relatively small image sizes. The authors propose SP-CNN, an architecture and a multiplexing algorithm that provides scalability to CNN applications. The authors demonstrate the proposed multiplexing algorithms over a set of six image processing benchmarks and present a performance analysis of SP-CNN.