TL;DR
This paper discusses the concept of transparent synchronous dataflow, highlighting its advantages in parallel and distributed computation, and its application in deep learning frameworks for automatic differentiation.
Contribution
It introduces the idea of transparent synchronous dataflow and explores its benefits and applications in systems modeling, optimization, and deep learning.
Findings
Enables efficient parallel computation
Facilitates automatic differentiation in deep learning
Supports distributed machine execution
Abstract
Dataflow programming is a popular and convenient programming paradigm in systems modelling, optimisation, and machine learning. It has a number of advantages, for instance the lacks of control flow allows computation to be carried out in parallel as well as in distributed machines. More recently the idea of dataflow graphs has also been brought into the design of various deep learning frameworks. They facilitate an easy and efficient implementation of automatic differentiation, which is the heart of modern deep learning paradigm. [abstract abridged]
Peer Reviews
No public reviews on file for this paper yet. If you reviewed it on a platform where reviews are public (OpenReview, ICLR, NeurIPS, ICML), you can paste yours below so the community can read it here.
Code & Models
Videos
No videos yet. Explain this paper in a talk, walkthrough, or lecture? Add one.
