DeltaNN: Assessing the Impact of Computational Environment Parameters on the Performance of Image Recognition Models

arXiv:2306.06208v5 Announce Type: replace-cross
Abstract: Image recognition tasks typically use deep learning and require enormous processing power, thus relying on hardware accelerators like GPUs and TPUs for fast, timely processing. Failure in real-time image recognition tasks can occur due to sub-optimal mapping on hardware accelerators during model deployment, which may lead to timing uncertainty and erroneous behavior. Mapping on hardware accelerators is done using multiple software components like deep learning frameworks, compilers, and device libraries, that we refer to as …

abstract accelerators arxiv computational cs.cv cs.lg cs.se cs.sy deep learning eess.sy environment failure gpus hardware image image recognition impact mapping parameters performance power processing real-time recognition tasks tpus type