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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 block diagram of the proposed binary memristor crossbar circuit with 4-bit 64 input channels. Each 4-bit input channel is composed of the true signal and the inverted signal.
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Figure 3: The block diagram of the proposed binary memristor crossbar circuit with 4-bit 64 input channels. Each 4-bit input channel is composed of the true signal and the inverted signal.

Mentions: Figure 3 shows a block diagram of the binary memristor crossbar circuit for recognizing five vowels: ‘a’, ‘i’, ‘u’, ‘e’, and ‘o’. The voice input is divided into 64 channels according to the voice's frequencies. The magnitude of each channel is sampled and digitized by 4 bits. The band-pass filtering, sampling, and digitization for the voice input are implemented by MATLAB simulation in this paper. The 4-bit 64 channel inputs that are obtained by MATLAB simulation are applied to the binary memristor crossbar array as shown in Figure 3. For recognizing five vowels, we need not only 4-bit 64 channel inputs but also their inverted values. Thus, the total number of channel inputs is as many as 128 with 64 channels of the true signals and 64 channels of the inverted signals. Each channel is composed of 4-bit binary values. In Figure 3, Ia,0 is the current of the ‘x1’ column in the crossbar array for recognizing ‘a’. Ia,1 is the current of the ‘x2’ column in the crossbar array for recognizing ‘a’. Similarly, Ia,2 and Ia,3 are the currents of the ‘x4’ and ‘x8’ columns in the ‘a’ crossbar array. Here, ‘x1’ means that the weight of this column current is as much as 1. In Figure 3, ‘x2’, ‘x4’, and ‘x8’ mean that the weight values are 2, 4, and 8, respectively, for the corresponding columns in the ‘a’ crossbar array. Here, Ia can be calculated with the weighted summation of 8Ia,3 + 4Ia,2 + 2Ia,1 + Ia,0. Similarly, Iu is the weighted summation of 8Iu,3 + 4Iu,2 + 2Iu,1 + Iu,0 for recognizing ‘u’. Io is the weighted summation of 8Io,3 + 4Io,2 + 2Io,1 + Io,0 for recognizing ‘o’. The currents of Ia, Ii, Iu, Ie, and Io are compared with each other in the winner-take-all circuit [19] to decide which vowel is the best match with the voice input as shown in Figure 3. Outputa, Outputi, Outputu, Outpute, and Outputo are the output signals of the winner-take-all circuit.


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

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

The block diagram of the proposed binary memristor crossbar circuit with 4-bit 64 input channels. Each 4-bit input channel is composed of the true signal and the inverted signal.
© Copyright Policy - open-access
Related In: Results  -  Collection

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

Figure 3: The block diagram of the proposed binary memristor crossbar circuit with 4-bit 64 input channels. Each 4-bit input channel is composed of the true signal and the inverted signal.
Mentions: Figure 3 shows a block diagram of the binary memristor crossbar circuit for recognizing five vowels: ‘a’, ‘i’, ‘u’, ‘e’, and ‘o’. The voice input is divided into 64 channels according to the voice's frequencies. The magnitude of each channel is sampled and digitized by 4 bits. The band-pass filtering, sampling, and digitization for the voice input are implemented by MATLAB simulation in this paper. The 4-bit 64 channel inputs that are obtained by MATLAB simulation are applied to the binary memristor crossbar array as shown in Figure 3. For recognizing five vowels, we need not only 4-bit 64 channel inputs but also their inverted values. Thus, the total number of channel inputs is as many as 128 with 64 channels of the true signals and 64 channels of the inverted signals. Each channel is composed of 4-bit binary values. In Figure 3, Ia,0 is the current of the ‘x1’ column in the crossbar array for recognizing ‘a’. Ia,1 is the current of the ‘x2’ column in the crossbar array for recognizing ‘a’. Similarly, Ia,2 and Ia,3 are the currents of the ‘x4’ and ‘x8’ columns in the ‘a’ crossbar array. Here, ‘x1’ means that the weight of this column current is as much as 1. In Figure 3, ‘x2’, ‘x4’, and ‘x8’ mean that the weight values are 2, 4, and 8, respectively, for the corresponding columns in the ‘a’ crossbar array. Here, Ia can be calculated with the weighted summation of 8Ia,3 + 4Ia,2 + 2Ia,1 + Ia,0. Similarly, Iu is the weighted summation of 8Iu,3 + 4Iu,2 + 2Iu,1 + Iu,0 for recognizing ‘u’. Io is the weighted summation of 8Io,3 + 4Io,2 + 2Io,1 + Io,0 for recognizing ‘o’. The currents of Ia, Ii, Iu, Ie, and Io are compared with each other in the winner-take-all circuit [19] to decide which vowel is the best match with the voice input as shown in Figure 3. Outputa, Outputi, Outputu, Outpute, and Outputo are the output signals of the winner-take-all circuit.

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.