Software-Hardware Codesign for Efficient Neural Network Acceleration
Kaiyuan Guo, Song Han, Song Yao, Yu Wang, Yuan Xie, Huazhong Yang · IEEE Micro · 2017
Designers making deep learning computing more efficient cannot rely solely on hardware. Incorporating software-optimization techniques such as model compression leads to significant power savings and performance improvement. This article provides an overview of DeePhi's technology flow, including compression, compilation, and hardware acceleration. Two accelerators, named Aristotle and Descartes, are designed to achieve extremely high energy efficiency for both client and datacenter applications with convolutional neural network and recurrent neural network, respectively.