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Memristor-Based Edge Computing of Blaze Block for Image Recognition.

Huanhuan Ran, Shiping Wen, Qian Li

IEEE Transactions on Neural Networks and Learning Systems

|December 29, 2020

Summary

A novel memristor-based edge computing system for image recognition uses specialized blaze blocks and depthwise separable convolutions. This system achieves 84.38% accuracy on CIFAR-10, offering significant advantages in computing resources, speed, and power consumption.

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Area of Science:

Computer Engineering
Artificial Intelligence
Materials Science

Background:

Edge computing requires efficient hardware for AI tasks like image recognition.
Memristor technology offers potential for low-power, high-density computing.

Purpose of the Study:

To propose a novel memristor-based edge computing system for image recognition.
To leverage memristor crossbars and depthwise separable convolutions for improved efficiency.

Main Methods:

Developed a memristive convolutional neural network (MCNN) with single and double memristive blaze blocks (SMBBs/DMBBs).
Utilized depthwise separable convolution neural networks (DwCNN) implemented on memristor crossbars (MC).
Incorporated batch normalization (BN) for accelerated convergence and a diode for output stability.

Main Results:

Achieved 84.38% accuracy on the CIFAR-10 dataset.
Demonstrated significant reductions in computing resources, calculation time, and power consumption.
Identified optimal parameters (2^8 multistate conductance, 8-bit quantization) for balancing power and cost.

Conclusions:

The proposed memristor-based circuit is an efficient solution for edge image recognition.
The system offers a favorable trade-off between performance, power consumption, and production cost.
This approach paves the way for more powerful and efficient edge AI hardware.