Before revision: Deep Neural Networks (DNNs) have gained significant interest in the recent past for plethora of applications such as imageand videoanalytics, language translation, and medical diagnosis. High memory bandwidth is required to keep up with the needs of data-intensive DNN applications when implemented on a von-Neumann hardware architecture as majority of the data resides in the main memory.
After revision: Deep Neural Networks (DNNs) have transformed the field of machine learning and are widely deployed in many applications involving image, video, speech and natural language processing. The increasing compute demands of DNNs have been widely addressed through Graphics Processing Units (GPUs) and specialized accelerators. However, as model sizes grow, these von Neumann architectures require very high memory bandwidth to keep the processing elements utilized as a majority of the data resides in the main memory.