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Neural Network Libraries: A Deep Learning Framework Designed from Engineers' Perspectives

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arxiv 2102.06725 v2 pith:Z3447ELR submitted 2021-02-12 cs.LG cs.CV

Takuya Narihira , Javier Alonsogarcia , Fabien Cardinaux , Akio Hayakawa , Masato Ishii , Kazunori Iwaki , Thomas Kemp , Yoshiyuki Kobayashi
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Lukas Mauch Akira Nakamura Yukio Obuchi Andrew Shin Kenji Suzuki Stephen Tiedmann Stefan Uhlich Takuya Yashima Kazuki Yoshiyama
This is my paper
classification cs.LGcs.CV
keywords deepdesignlearningnetworkcompatibilitydesignedframeworklibraries
verification ladder T0 review T1 audit T2 compute T3 formal
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While there exist a plethora of deep learning tools and frameworks, the fast-growing complexity of the field brings new demands and challenges, such as more flexible network design, speedy computation on distributed setting, and compatibility between different tools. In this paper, we introduce Neural Network Libraries (https://nnabla.org), a deep learning framework designed from engineer's perspective, with emphasis on usability and compatibility as its core design principles. We elaborate on each of our design principles and its merits, and validate our attempts via experiments.

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Cited by 2 Pith papers

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