LAUSR

With the rapid advancement in the complementary metal-oxide-semiconductor (CMOS) technology, the performance of neuromorphic computing systems that employed the emerging devices has entered a new era of high accuracy and energy efficient operations. As one of the most fundamental components in computing systems, encoding scheme characterizes the relationship between the stimulus and the individual or ensemble neuronal responses. Although rate encoding is easier to implement and with high error tolerance, temporal encoding offers high data density and energy efficiency. In this paper, an inter-spike interval (ISI)-based resistive crossbar neuromorphic design, built with the standard CMOS technology, is proposed. Our proposed decoder exhibits not only computation accuracy but also robustness. Another worth-mentioning achievement is the data compressor in our work, which is based on the spike temporal encoding scheme. In this paper, design and performance analysis for proposed ISI-based resistive crossbar are elaborated; as well as the application of our proposed design. 24 patterns are adopted to encode the sensory information which are successfully represented by seven inter-spike intervals leading to significantly high compression rate. To evaluate the performance, a test bench of video frames consisting one person rotating her head from 0° to 75° with an increment of 15° has been employed. The results showed that the ISI code has better performance in both recognition rate and converging speed.