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Neuromorphic crossbar circuit with nanoscale filamentary-switching binary memristors for speech recognition.

Truong SN, Ham SJ, Min KS - Nanoscale Res Lett (2014)

Bottom Line: The binary memristors which are based on filamentary-switching mechanism can be found more popularly and are easy to be fabricated than analog memristors that are rare in materials and need a more complicated fabrication process.From the statistical simulation, the recognition rate of the binary memristor crossbar is estimated to be degraded very little from 89.2% to 80%, though the percentage variation in memristance is increased very much from 0% to 15%.In contrast, the analog memristor crossbar loses its recognition rate significantly from 96% to 9% for the same percentage variation in memristance.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Electrical Engineering, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul 136-702, South Korea.

ABSTRACT
In this paper, a neuromorphic crossbar circuit with binary memristors is proposed for speech recognition. The binary memristors which are based on filamentary-switching mechanism can be found more popularly and are easy to be fabricated than analog memristors that are rare in materials and need a more complicated fabrication process. Thus, we develop a neuromorphic crossbar circuit using filamentary-switching binary memristors not using interface-switching analog memristors. The proposed binary memristor crossbar can recognize five vowels with 4-bit 64 input channels. The proposed crossbar is tested by 2,500 speech samples and verified to be able to recognize 89.2% of the tested samples. From the statistical simulation, the recognition rate of the binary memristor crossbar is estimated to be degraded very little from 89.2% to 80%, though the percentage variation in memristance is increased very much from 0% to 15%. In contrast, the analog memristor crossbar loses its recognition rate significantly from 96% to 9% for the same percentage variation in memristance.

No MeSH data available.


The conceptual signal flow of a neuromorphic speech-recognition system with memristor crossbar array.
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Figure 1: The conceptual signal flow of a neuromorphic speech-recognition system with memristor crossbar array.

Mentions: A conceptual diagram of a neuromorphic speech-recognition system is shown in Figure 1. In Figure 1, a voice signal enters the cochlea first. In the cochlea, the voice input is divided into many different channels according to the voice's frequencies. Basically, the cochlea is modeled as a group of band-pass filters, where the voice input is divided and filtered by a band-pass filter array with the frequency range from 20 Hz to 20 KHz [8,9]. Each channel in the band-pass filter array can deliver a different band signal to the crossbar circuit as shown in Figure 1. Here, we assume that our goal is recognizing five vowels: ‘a’, ‘i’, ‘u’, ‘e’, and ‘o’, from the input of a human voice. To do so, the voice input is filtered and sampled as the cochlea does. Then, the filtered and sampled signals go into the memristor crossbar circuit as shown in Figure 1, where the voice input is compared with the previously trained patterns of five different vowels which are already stored in the memristor crossbar array. By doing so, we can decide which vowel among the five different vowels is the best match with the voice input to the crossbar array.


Neuromorphic crossbar circuit with nanoscale filamentary-switching binary memristors for speech recognition.

Truong SN, Ham SJ, Min KS - Nanoscale Res Lett (2014)

The conceptual signal flow of a neuromorphic speech-recognition system with memristor crossbar array.
© Copyright Policy - open-access
Related In: Results  -  Collection

License
Show All Figures
getmorefigures.php?uid=PMC4256962&req=5

Figure 1: The conceptual signal flow of a neuromorphic speech-recognition system with memristor crossbar array.
Mentions: A conceptual diagram of a neuromorphic speech-recognition system is shown in Figure 1. In Figure 1, a voice signal enters the cochlea first. In the cochlea, the voice input is divided into many different channels according to the voice's frequencies. Basically, the cochlea is modeled as a group of band-pass filters, where the voice input is divided and filtered by a band-pass filter array with the frequency range from 20 Hz to 20 KHz [8,9]. Each channel in the band-pass filter array can deliver a different band signal to the crossbar circuit as shown in Figure 1. Here, we assume that our goal is recognizing five vowels: ‘a’, ‘i’, ‘u’, ‘e’, and ‘o’, from the input of a human voice. To do so, the voice input is filtered and sampled as the cochlea does. Then, the filtered and sampled signals go into the memristor crossbar circuit as shown in Figure 1, where the voice input is compared with the previously trained patterns of five different vowels which are already stored in the memristor crossbar array. By doing so, we can decide which vowel among the five different vowels is the best match with the voice input to the crossbar array.

Bottom Line: The binary memristors which are based on filamentary-switching mechanism can be found more popularly and are easy to be fabricated than analog memristors that are rare in materials and need a more complicated fabrication process.From the statistical simulation, the recognition rate of the binary memristor crossbar is estimated to be degraded very little from 89.2% to 80%, though the percentage variation in memristance is increased very much from 0% to 15%.In contrast, the analog memristor crossbar loses its recognition rate significantly from 96% to 9% for the same percentage variation in memristance.

View Article: PubMed Central - HTML - PubMed

Affiliation: School of Electrical Engineering, Kookmin University, 77, Jeongneung-ro, Seongbuk-gu, Seoul 136-702, South Korea.

ABSTRACT
In this paper, a neuromorphic crossbar circuit with binary memristors is proposed for speech recognition. The binary memristors which are based on filamentary-switching mechanism can be found more popularly and are easy to be fabricated than analog memristors that are rare in materials and need a more complicated fabrication process. Thus, we develop a neuromorphic crossbar circuit using filamentary-switching binary memristors not using interface-switching analog memristors. The proposed binary memristor crossbar can recognize five vowels with 4-bit 64 input channels. The proposed crossbar is tested by 2,500 speech samples and verified to be able to recognize 89.2% of the tested samples. From the statistical simulation, the recognition rate of the binary memristor crossbar is estimated to be degraded very little from 89.2% to 80%, though the percentage variation in memristance is increased very much from 0% to 15%. In contrast, the analog memristor crossbar loses its recognition rate significantly from 96% to 9% for the same percentage variation in memristance.

No MeSH data available.