https://dl.acm.org/citation.cfm?id=3321441 (click on "PDF" link to read)
Abstract: "In this paper we argue that systems for numerical computing are stuck in a local basin of performance and programmability. Systems researchers are doing an excellent job improving the performance of 5-year-old benchmarks, but gradually making it harder to explore innovative machine learning research ideas. We explain how the evolution of hardware accelerators favors compiler back ends that hyper-optimize large monolithic kernels, show how this reliance on high-performance but inflexible kernels reinforces the dominant style of programming model, and argue these programming abstractions lack expressiveness, maintainability, and modularity; all of which hinders research progress. We conclude by noting promising directions in the field, and advocate steps to advance progress towards high-performance general purpose numerical computing systems on modern accelerators."
The main example the authors use to illustrate these issues is capsule networks, first proposed two years ago.[a]
To date, no one has been able to develop a high-performance implementation of capsule networks. At present, the best-performing implementations in Tensorflow and PyTorch must copy, rearrange, and materialize to memory two orders of magnitude more data than necessary, due to the issues raised by the authors. See sections 1 and 2 of the paper for the gory details.
Two orders of magnitude. That is pathetic.