An Autonomous Error-Tolerant Architecture Featuring Self-reparation for Convolutional Neural Networks

• Convolutional neural networks are widely used in artificial intelligence and Internet of Things area. As the scale of convolutional neural network expands, more and more processing units are provided for it. The systems are easy prone to error, and any computing problems in any layer of the network will lead to wrong output results. Traditional multimode redundancy methods make the systems more complex, and increase power consumption. This paper proposes an autonomous error-tolerant architecture for convolutional neural networks. Taking the LeNet-5 as an example, the network layers of CNN are mapped on the AET architecture, an error-tolerant synapse is designed to discover the errors, an active evolution scheme is designed to handle unrecoverable errors and implement network reconfiguration. This design is implemented on FPGA, and the experimental results show that this architecture can realize effective error tolerance for convolutional neural network and has fast error recovery ability under the premise of ensuring the same recognition accuracy.

Ämnesord

NATURVETENSKAP  -- Data- och informationsvetenskap -- Datorseende och robotik (hsv//swe)

NATURAL SCIENCES  -- Computer and Information Sciences -- Computer Vision and Robotics (hsv//eng)

Nyckelord

autonomous error-tolerant

convolutional neural network

FPGA

LeNet-5

self-repair

Convolution

Errors

Network architecture

Network layers

Springs (components)

Error tolerance

Error tolerant

Multimodes

Network re-configuration

Processing units

Recognition accuracy

Convolutional neural networks

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