[SPCL_Bcast] Optimization of Data Movement for Convolutional Neural Networks

Speaker: P. Sadayappan
Venue: SPCL_Bcast, recorded on 7 October, 2021
Abstract: Convolutional Neural Networks (CNNs) are central to Deep Learning. The optimization of CNNs has therefore received significant attention. Minimizing data movement is critical to performance optimization. This talk will address the minimization of data movement for CNNs in two scenarios. In the first part of the talk, the optimization of tile loop permutations and tile size selection will be discussed for executing CNNs on multicore CPUs. Most efforts on optimization of tiling for CNNs have either used heuristics or limited search over the huge design space. We show that a comprehensive design space exploration is feasible via analytical modeling. In the second part of the talk, communication minimization for executing CNNs on distributed systems will be discussed.