A Survey of Artificial Neural Network Computing Systems

An artificial neural network (ANN) is currently used in multiple different applications such as bio-medicine, finance, Internet, and mobile networks. Since their inception, many advances have taken place introducing new models and features. Such progress resulted in different ANN models and most importantly different types of implementation, which vary from software (SW) to hardware (HW) following specific development principles. Researchers have been working significantly the last decade in this area tackling with different aspects of ANN’s implementations. In this survey, we present the progress of ANN in terms of implementation as part of computing platforms. Thus, we present the ANN-enabled computing platforms in terms of algorithmic models, computing architectures, and SW/HW implementations. This work concludes with open challenges and lessons learned in order to summarize what is potentially useful for further research in the area of ANN computing platforms with a wide spectrum of applications. An artificial neural network (ANN) is considered the key element of future computing systems applied to different domains. While the algorithmic design of an ANN is one of the major engineering elements, the implementation of ANN is equally important with many difficulties that should be overcome by future engineers. This survey aims to provide a comprehensive tutorial about the ANN-enabled computing systems, i.e., computing architectures with embedded artificial intelligence (AI). Starting with the ANN models and their applications, the survey provides a taxonomy of the types of ANN computing systems. Both SW and HW implementations are provided for each of those types, which highlight the key architectural elements as well as the performance of the ANN-enabled computing systems. Open challenges and lessons learned follow to provide a discussion for future research in the area of AI computing systems.