The Role and Potential of CPUs in Deep Learning

Deep learning (DL) applications have unique architectural characteristics and efficiency requirements. Hence, the choice of computing system has a profound impact on how large a piece of the DL pie a user can finally enjoy. Even though accelerators may provide higher throughput than general-purpose computing systems (CPUs), there are several other metrics and usage scenarios on which CPUs are preferred or are superior. A recent survey paper I've coauthored with Poonam Rajput and Sreenivas Subramoney (A Survey of Deep Learning on CPUs: Opportunities and Co-optimizations) highlights the strengths of CPUs in DL, and identifies opportunities for further optimization. Sparse DNNs are inefficient on massively parallel processors because of their irregular memory accesses and inability to leverage optimizations such as cache tiling and vectorization.