Abstract
Computational storage allows data to reside close to processing power, thus allowing processing tasks to be in-line with data accesses. In this informative video presentation from the SNIA sponsored Computational Storage Track at Flash Memory Summit 2020, SNIA expert Stephen Bates describes real world computational storage applications that illustrate inference engines that do pre-processing for machine learning, and distributed processing engines performing compression, sorting and profiling. He also comments on promising directions to explore for the future.