The Framework Tax: Disparities Between Inference Efficiency in Research and Deployment

Increased focus on the deployment of machine learning systems has led to rapid improvements in hardware accelerator performance and neural network model efficiency. However, the resulting reductions in floating point operations and increases in computational throughput of accelerators have not directly translated to improvements in real-world inference latency. We demonstrate that these discrepancies can be largely attributed to mis-alignments between model architectures and the capabilities of underlying hardware due to bottlenecks introduced by deep learning frameworks. We denote this phenomena as the \textit{framework tax}, and observe that the disparity is growing as hardware speed increases over time. In this work, we examine this phenomena through a series of case studies analyzing the effects of model design decisions, framework paradigms, and hardware platforms on total model latency. Based on our findings, we provide actionable recommendations to ML researchers and practitioners aimed at narrowing the gap between efficient ML model research and practice.