Neural Network Libraries: A Deep Learning Framework Designed from Engineers' Perspectives
This work addresses usability and compatibility issues for engineers in deep learning, but it is incremental as it builds on existing frameworks.
The authors tackled the challenges of flexible network design, distributed computation, and tool compatibility in deep learning by introducing Neural Network Libraries, a framework designed with usability and compatibility as core principles, and validated their approach through experiments.
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.