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Memristor-Based Edge Computing of Blaze Block for Image Recognition | IEEE Journals & Magazine | IEEE Xplore

Memristor-Based Edge Computing of Blaze Block for Image Recognition

Publisher: IEEE

Abstract:

In this article, a novel edge computing system is proposed for image recognition via memristor-based blaze block circuit, which includes a memristive convolutional neural...View more

Abstract:

In this article, a novel edge computing system is proposed for image recognition via memristor-based blaze block circuit, which includes a memristive convolutional neural network (MCNN) layer, two single-memristive blaze blocks (SMBBs), four double-memristive blaze blocks (DMBBs), a global Avg-pooling (GAP) layer, and a memristive full connected (MFC) layer. SMBBs and DMBBs mainly utilize the depthwise separable convolution neural network (DwCNN) that can be implemented with a much smaller memristor crossbar (MC). In the backward propagation, we use batch normalization (BN) layers to accelerate the convergence. In the forward propagation, this circuit combines DwCNN layers/CNN layers with nonseparate BN layers, which means that the required number of operational amplifiers is cut by half as long as the greatly reduced power consumption. A diode is added after the rectified linear unit (ReLU) layer to limit the output of the circuit below the threshold voltage V_{t} of the memristor; thus, the circuit is more stable. Experiments show that the proposed memristor-based circuit achieves an accuracy of 84.38% on the CIFAR-10 data set with advantages in computing resources, calculation time, and power consumption. Experiments also show that, when the number of multistate conductance is 2 8 and the quantization bit of the data is 8, the circuit can achieve its best balance between power consumption and production cost.
Page(s): 2121 - 2131
Date of Publication: 29 December 2020

ISSN Information:

PubMed ID: 33373307
Publisher: IEEE

Funding Agency:


References

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