|Title||Memristor-based approximated computation|
|Publication Type||Conference Paper|
|Year of Publication||2013|
|Authors||B Li, Y Shan, M Hu, Y Wang, Y Chen, and H Yang|
|Conference Name||Proceedings of the International Symposium on Low Power Electronics and Design|
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 22× power efficiency improvements than its pure digital implementation counterpart. © 2013 IEEE.