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

November 07, 2023

Abstract

Increased focus on the computational efficiency of NLP systems has motivated the design of efficient model architectures and improvements to underlying hardware accelerators. However, the resulting increases in computational throughput and reductions in floating point operations have not directly translated to improvements in wall-clock inference latency. We demonstrate that these discrepancies can be largely attributed to bottlenecks introduced by deep learning frameworks. We denote this phenomenon as the framework tax, and observe that the disparity is growing as hardware speed increases over time. In this work, we examine this phenomenon through a series of case studies analyzing the effects of model design decisions, framework paradigms, and hardware platforms on total model latency.

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AUTHORS

Written by

Jared Fernandez

Jacob Kahn

Clara Na

Yonatan Bisk

Emma Strubell

Publisher

EMNLP

Research Topics

Systems Research

Natural Language Processing (NLP)

Computer Vision

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