ResearchTrend.AI
  • Papers
  • Communities
  • Events
  • Blog
  • Pricing
Papers
Communities
Social Events
Terms and Conditions
Pricing
Parameter LabParameter LabTwitterGitHubLinkedInBlueskyYoutube

© 2025 ResearchTrend.AI, All rights reserved.

  1. Home
  2. Papers
  3. 2102.06725
18
10

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

12 February 2021
T. 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
T. Yashima
K. Yoshiyama
ArXivPDFHTML
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.

View on arXiv
Comments on this paper