Recently, spiking neural networks have gained attention owing to their energy efficiency. Allto-all spike-time dependent plasticity is a popular learning algorithm for spiking neural networks because it is suitable for nondifferentiable spike event-based learning and requires fewer computations than backpropagation-based algorithms. However, the hardware implementation of all-to-all spike-time dependent plasticity is limited by the large storage area required for spike history and large energy consumption caused by frequent memory access. We propose a time-step scaled spike-time dependent plasticity to reduce the storage area required for spike history by reducing the area of the spike-time dependent plasticity learning circuit by 60% and a post-neuron spike-referred spike-time dependent plasticity to reduce the energy consumption by 99.1% by efficiently accessing the memory while learning. The accuracy of Modified National Institute of Standards and Technology image classification degraded by less than 2% when both time-step scaled spike-time dependent plasticity and post-neuron spike-referred spike-time dependent plasticity were applied. Thus, the proposed hardware-friendly spike-time dependent plasticity algorithms make all-to-all spike-time dependent plasticity implementable in more compact areas while reducing energy consumption and experiencing insignificant accuracy degradation. INDEX TERMS Spike-time dependent plasticity (STDP), Time-step scaled STDP (TS-STDP), Postneuron spike-referred STDP (PR-STDP), Spiking neural network (SNN) Wij Learning neuron Synaptic weight Off-chip On-chip Pre-neuron spike Post-neuron spike ... ... FIGURE 1. Concept of SNN and its structure.

This paper presents a learning-based visual inspection method that addresses the need for an improved adaptability of a visual inspection system for parts verification in panorama sunroof assembly lines. It is essential to ensure that the many parts required (bolts and nuts, etc.) are properly installed in the PLC sunroof manufacturing process. Instead of human inspectors, a visual inspection system can automatically perform parts verification tasks to assure that parts are properly installed while rejecting any that are improperly assembled. The proposed visual inspection method is able to adapt to changing inspection tasks and environmental conditions through an efficient learning process. The proposed system consists of two major modules: learning mode and test mode. The SVM (Support Vector Machine) learning algorithm is employed to implement part learning and verification. The proposed method is very robust for changing environmental conditions, and various experimental results show the effectiveness of the proposed method.

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