  With the success of deep learning in various fields and the advent of
numerous Internet of Things (IoT) devices, it is essential to lighten models
suitable for low-power devices. In keeping with this trend, MicroNet Challenge,
which is the challenge to build efficient models from the view of both storage
and computation, was hosted at NeurIPS 2019. To develop efficient models
through this challenge, we propose a framework, coined as SIPA, consisting of
four stages: Searching, Improving, Pruning, and Accelerating. With the proposed
framework, our team, OSI AI, compressed 334x the parameter storage and 357x the
math operation compared to WideResNet-28-10 and took 4th place in the CIFAR-100
track at MicroNet Challenge 2019 with the top 10% highly efficient computation.
Our source code is available from https://github.com/Lee-Gihun/MicroNet_OSI-AI.
