**7. Conclusions**

In this chapter, the design aspect of our analog DFR system with the analogue electronic circuit model of biological neuron is discussed. By mimicking how human beings process information, our analog DFR system adapts the spiking temporal information processing technique and a nonlinear activation function to project input patterns onto higher dimensional spaces. From measurement results, our analog DFR system demonstrates richness in dynamic behaviors, closely mimicking the biological neurons with delay property. By naturally perform these neuron-like operations, our analog DFR system is capable to nonlinearly project input patterns onto higher dimensional spaces for the classification while operating at the edge-ofchaos region with merely 526 μW of power consumption. Experimental results on the chaotic time series prediction and the video frame recognition demonstrate the high recognition accuracy even with noise, making our analog DFR system a candidate for low power intelligence applications.

**25**

**Author details**

Kangjun Bai and Yang Yi\*

provided the original work is properly cited.

\*Address all correspondence to: yangyi8@vt.edu

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

The Bradley Department of Electrical and Computer Engineering, Virginia Polytechnic Institute and State University, Blacksburg, Virginia, USA

*Opening the "Black Box" of Silicon Chip Design in Neuromorphic Computing*

*DOI: http://dx.doi.org/10.5772/intechopen.83832*

*Opening the "Black Box" of Silicon Chip Design in Neuromorphic Computing DOI: http://dx.doi.org/10.5772/intechopen.83832*
