Memory system characterization of deep learning workloads
Zeshan A. Chishti, Berkin Akin · Proceedings of the International Symposium on Memory Systems · 2019
Deep neural networks (DNNs) have emerged as the prevalent approach for implementing learning tasks in many application domains. As DNN models become increasingly complex, the large amount of data generated during network computations exerts substantial pressure on the capacity and bandwidth of the memory subsystem. Consequently, memory hierarchy is quickly becoming a major bottleneck for DNN performance scaling.