Deep Learning Compilers: Architecture, Optimization, and Empirical Characterization

As deep learning (DL) models become increasingly complex and hardware architectures grow more heterogeneous, DL compilers play a critical role in optimizing computational efficiency and enabling scalable deployment. This paper presents a systematic analysis of DL compilers, covering their architectural evolution, compilation workflows, optimization techniques, and emerging research directions. We organize DL compiler architectures into frontend, middle-end, and backend stages, emphasizing the r…

Source: arXiv cs.AR (Hardware Architecture), published 2026-11-04. The summary is the Hub's own; the full story is at the source. Report a rights or accuracy concern