Optimizing the efficiency of deep learning through accelerator virtualization.

Training deep learning models often occupies entire compute clusters, built solely for this purpose, for days or even weeks at a time. There exists a large body of work on approaches for improving training performance, ranging from novel algorithms to full custom hardware accelerators. Offering compute capabilities of multiple teraflops (trillion floating point operations per second), graphics pro...