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arXiv · 2102.06725

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

Takuya Narihira·Javier Alonsogarcia·Fabien Cardinaux·Akio Hayakawa·Masato Ishii·Kazunori Iwaki·Thomas Kemp·Yoshiyuki Kobayashi·Lukas Mauch·Akira Nakamura·Yukio Obuchi·Andrew Shin·Kenji Suzuki·Stephen Tiedmann·Stefan Uhlich·Takuya Yashima·Kazuki Yoshiyama
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Abstract

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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Takuya Narihira, Javier Alonsogarcia, Fabien Cardinaux, Akio Hayakawa, Masato Ishii, Kazunori Iwaki, Thomas Kemp, Yoshiyuki Kobayashi, Lukas Mauch, Akira Nakamura, Yukio Obuchi, Andrew Shin, Kenji Suzuki, Stephen Tiedmann, Stefan Uhlich, Takuya Yashima, Kazuki Yoshiyama. 2021-02-12. Neural Network Libraries: A Deep Learning Framework Designed from Engineers' Perspectives. https://arxiv.org/abs/2102.06725

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