International Journal of Engineering Science Invention ISSN (Online): 2319 – 6734, ISSN (Print): 2319 – 6726 www.ijesi.org || Volume 5 Issue 5 || May 2016 || PP.1-7 www.ijesi.org 1 | Page New binary memristor crossbar architecture based neural networks for speech recognition Van-Tien Nguyen 1 , Minh-Huan Vo 2 1,2 Department of Electrical Electronic Engineering, HCMC University of Technology and Education, Viet Nam. ABSTRACT : In this paper, we propose a new binary memristor crossbar architecture based neural networks for speech recognition. The circuit can recognize five vowels. The proposed crossbar is tested by 1,000 speech samples and recognized 94% of the tested samples. We use Monte Carlo simulation to estimate recognitition rate. The percentage variation in memristance is increased from 0% to 15%, the recognition rate is degraded from 94% to 82%. KEYWORDS – Memristors, Crossbar, Speech recognition, Binary memristors. I. INTRODUCTION According to Moore's Law, transistor count per chip will double within two years. The IC technology development in recent years has shown the validity of Moore's law. However, according to estimates in the near future, the technology will reach the limit of Moore's Law, which means that the chip size will reach critical values to ensure accuracy and stability. So, there are many new methods being studied to replace Moore's law. And memristor of Leon. Chua was mentioned in 1971 [1], which was completed by Stanley- Williams in 2008 [2]. The future of technology has opened up new development. This technology is even better than CMOS technology has been thriving. Anyone with a basic knowledge in electrical engineering knows that there are four fundamental circuit variables: Current i, Voltage v, Charge q, and Flux f. Then it is clear that with these four parameters, there can be six possible combinations for relating them to each other. So far we have complete understanding and control over five of these combinations in which three of them are passive twoterminal fundamental circuit elements, namely the resistor R, the capacitor C and the inductor L. Unlike the active components which can generate energy, these three components are passive elements which are only capable of storing or dissipating energy but not generating it. The relationship between 'voltage and current', 'voltage and charge', and 'current and flux' are defined by a resistor, capacitor and an inductor, respectively. No device was there to relate the charge and the magnetic flux until Leon Chua introduced his new circuit element called “memristor” . In 2008, a research group at HP Labs lead by Stanley Williams succeeded to fabricate the device in nanometer scale. Since then, the research being conducted on memristors gained momentum and the number of publications have boosted quite rapidly. Memristor have two types, are analog memristors and binary memristors. The analog memristor can change the value memristance depend on voltage or electric current applied to it. However, installing memristance value is difficult, not exactly. On the contrary, memristance of binary memristor is easy to install, and more exact. Binary memristors have two state either a high resistance state (HRS) or a low resistance state (LRS), so they can be stored only„1‟ or „0‟ in binary memristors. Recent research focuses on using crossbar architecture to simulate synaptic systems. Thus, an application uses memristor for speech recognition [3]. τur research focuses on recognizing five vowels: „a‟, „e‟, „i‟, „o‟ and „u‟ from the human voice. To do this, First, a voice signal will be extracted features by MFCC [4]. There are 48 feature values. Then, they are trained by neutral network to recognize 5 vowels. After training, weightings are quantized in 4 bits, their values were stored in binary memristor crossbar circuit. The memristor can achieve either a high-resistance state (HRS) or a low-resistance state (LRS). It means that memristor can store „1‟ and „0‟ with two states. This memristor plays a role as a 2 -terminal switch to change the resistance between high resistance state (HRS, logic “0”) and low resistance state (LRS, logic “1”). By doi ng so, we can recognize each vowel by multiplication of input signal and weight stored in binary memrisor. The summation of the multiplication results decides the biggest output among 5 outputs that will represent input signal. We suggest a new binary memristor crossbar circuit based neural network model for recognizing five vowels. In addition, statistical simulations are performed, and the simulation results are discussed and finally summarized in this paper.