Concrete: A Per-layer Configurable Framework for Evaluating DNN with Approximate Operators
Zheyu Liu, Guihong Li, Fei Qiao, Qi Wei, Ping Jin, Xinjun Liu, Huazhong Yang
Abstract
Approximate computing has drawn considerable attention to both academia and industry in the area of DNN hardware. Despite substantial efforts to design approximate circuits and building blocks, the resilience of DNN layers and structures remains an untapped field to explore. This paper presents an efficient framework to evaluate DNN resilience with fine-grained approximate operations, such as multipliers, adders and low-bit operators. The framework can execute large-scale approximate DNNs with relatively less time overhead. Massive experiments are conducted with the proposed framework to reveal the relationship between network structures and error tolerance. Additionally, a case study of fine-tuning the approximate DNN is presented.
BibTeX
@inproceedings{icassp2019_concreteaperlaye,
title = {Concrete: A Per-layer Configurable Framework for Evaluating DNN with Approximate Operators},
author = {Zheyu Liu and Guihong Li and Fei Qiao and Qi Wei and Ping Jin and Xinjun Liu and Huazhong Yang},
booktitle = {ICASSP 2019},
year = {2019}
}