Jeongha Lee1, Hyokyung Bahn2,*
CMC-Computers, Materials & Continua, Vol.76, No.1, pp. 607-629, 2023, DOI:10.32604/cmc.2023.039236
Abstract Due to the recent trend of software intelligence in the Fourth Industrial Revolution, deep learning has become a mainstream workload for modern computer systems. Since the data size of deep learning increasingly grows, managing the limited memory capacity efficiently for deep learning workloads becomes important. In this paper, we analyze memory accesses in deep learning workloads and find out some unique characteristics differentiated from traditional workloads. First, when comparing instruction and data accesses, data access accounts for 96%–99% of total memory accesses in deep learning workloads, which is quite different from traditional workloads. Second, when comparing read and write accesses,… More >