Quantum mechanics

Function (biology)

Network planning and design

Engineering

Philosophy

Biology

Theoretical computer science

Work in process

Operating system

Computer science

Simple (philosophy)

Radiative transfer

Distributed computing

Evolutionary biology

Computer network

Engineering design process

Mechanical engineering

Physics

Computer architecture

Parametrization (atmospheric modeling)

Design process

Epistemology

Operations management

Space (punctuation)

In this work, we present a new network design paradigm. Our goal is to help advance the understanding of network design and discover design principles that generalize across settings. Instead of focusing on designing individual network instances, we design network design spaces that parametrize populations of networks. The overall process is analogous to classic manual design of networks, but elevated to the design space level. Using our methodology we explore the structure aspect of network design and arrive at a low-dimensional design space consisting of simple, regular networks that we call RegNet. The core insight of the RegNet parametrization is surprisingly simple: widths and depths of good networks can be explained by a quantized linear function. We analyze the RegNet design space and arrive at interesting findings that do not match the current practice of network design. The RegNet design space provides simple and fast networks that work well across a wide range of flop regimes. Under comparable training settings and flops, the RegNet models outperform the popular EfficientNet models while being up to 5x faster on GPUs.

Designing Network Design Spaces