Automatic Solder Joint Inspection
IEEE Transactions on Pattern Analysis and Machine Intelligence
Multilayer feedforward networks are universal approximators
Neural Networks
The implementation of a low-cost production-line inspection system
Computer-Aided Engineering Journal
Sufficient conditions on general fuzzy systems as function approximators
Automatica (Journal of IFAC)
Deterministic bit-stream digital neurons
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
A digital hardware pulse-mode neuron with piecewise linear activation function
IEEE Transactions on Neural Networks
Implementation of a new neurochip using stochastic logic
IEEE Transactions on Neural Networks
Real-time computing platform for spiking neurons (RT-spike)
IEEE Transactions on Neural Networks
IEEE Transactions on Neural Networks
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The visual inspection of defects in products is heavily dependent on human experience and instinct. In this situation, it is difficult to reduce the production costs and to shorten the inspection time and hence the total process time. Consequently people involved in this area desire an automatic inspection system. In this paper, we propose a hardware neural network, which is expected to provide high-speed operation for automatic inspection of products. Since neural networks can learn, this is a suitable method for self-adjustment of criteria for classification. To achieve high-speed operation, we use parallel and pipelining techniques. Furthermore, we use a piecewise linear function instead of a conventional activation function in order to save hardware resources. Consequently, our proposed hardware neural network achieved 6GCPS and 2GCUPS, which in our test sample proved to be sufficiently fast.