Abstract
Hardware implementation of spiking neural networks (SNN) has been the focus of many previous works due to its higher execution speed. A block-based SNN architecture with a simple spiking neuron model is proposed in this paper. Compared to traditional spiking neuron models, the proposed model simplifies the equation of the membrane potential for ease of hardware implementation. The block-based SNN architecture also makes the hardware implementation more scalable and simplifies floorplanning. Deme genetic algorithm (GA) was applied for training the SNN model, and a population encoding scheme was used for spike time conversion. Two case studies were carried out to verify the functionality of the proposed model, namely number recognition and Fisher Iris classification. Experimental results showed that the proposed SNN model with deme GA was able to achieve comparable or higher classification accuracy than previous works.
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