Memristor-based approximated computation

The cessation of Moore's Law has limited further improvements in power efficiency. In recent years, the physical realization of the memristor has demonstrated a promising solution to ultra-integrated hardware realization of neural networks, which can be leveraged for better performance and powe...

Full description

Saved in:
Bibliographic Details
Published inProceedings of the 2013 International Symposium on Low Power Electronics and Design pp. 242 - 247
Main Authors Li, Boxun, Shan, Yi, Hu, Miao, Wang, Yu, Chen, Yiran, Yang, Huazhong
Format Conference Proceeding
LanguageEnglish
Published Piscataway, NJ, USA IEEE Press 04.09.2013
SeriesACM Conferences
Subjects
Online AccessGet full text
ISBN1479912352
9781479912353
DOI10.5555/2648668.2648729

Cover

More Information
Summary:The cessation of Moore's Law has limited further improvements in power efficiency. In recent years, the physical realization of the memristor has demonstrated a promising solution to ultra-integrated hardware realization of neural networks, which can be leveraged for better performance and power efficiency gains. In this work, we introduce a power efficient framework for approximated computations by taking advantage of the memristor-based multilayer neural networks. A programmable memristor approximated computation unit (Memristor ACU) is introduced first to accelerate approximated computation and a memristor-based approximated computation framework with scalability is proposed on top of the Memristor ACU. We also introduce a parameter configuration algorithm of the Memristor ACU and a feedback state tuning circuit to program the Memristor ACU effectively. Our simulation results show that the maximum error of the Memristor ACU for 6 common complex functions is only 1.87% while the state tuning circuit can achieve 12-bit precision. The implementation of HMAX model atop our proposed memristor-based approximated computation framework demonstrates 22X power efficiency improvements than its pure digital implementation counterpart.
ISBN:1479912352
9781479912353
DOI:10.5555/2648668.2648729