An ASIC-Based Artificial Neural Network Applied Real-time Speech Recognition SOPC

  • Lam D. Pham University of Technology, Ho Chi Minh City, Vietnam
  • Hieu M. Nguyen University of Technology, Ho Chi Minh City, Vietnam
  • Du N. N. T. Nguyen University of Technology, Ho Chi Minh City, Vietnam
  • Trang Hoang University of Technology, Ho Chi Minh City, Vietnam


Artificial Neural Network (ANN) is promoted to one of major schemes applied in pattern recognition area. Indeed, many approaches to software-based platforms have proven great performance of ANN. However, developing pattern recognition systems integrating ANN hardware-based architecture has been limited not only by the silicon requirements such as frequency, area, power, or resource but also by high accuracy and real-time applications strictly. Although a considerable number of ANN hardware-based architectures have been proposed currently, they have experienced a deprivation of functions due to both small configurations and ability of reconfiguration. Consequently, achieving an effective ANN hardware-based architecture so as to adapt to not only strict accuracy, enormous configures, or silicon area but also real-time criterion in pattern recognition systems has been really challenged. To tackle these issues, this work has proposed a dynamic structure of three-layer ANN architecture being able to reconfigure for adapting to various real-time applications. What is more, a complete SOPC system integrating proposed ANN hardware has also implemented to apply Vietnamese speech recognition automatically to confirm high recognition probability around 95.2 % towards 20 Vietnamese discrete words. Moreover, experiment results on such ASIC-based architecture have witnessed maximum frequency at 250 MHz on 130nm technology as well as great ability of reconfiguration.


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How to Cite
D. PHAM, Lam et al. An ASIC-Based Artificial Neural Network Applied Real-time Speech Recognition SOPC. Journal of Science and Technology: Issue on Information and Communications Technology, [S.l.], v. 2, n. 1, p. 38-48, aug. 2016. ISSN 1859-1531. Available at: <>. Date accessed: 24 mar. 2023. doi: