Digital Implementation of a Spiking CNN for Tumor Detection

Reza Karimi


In this paper, an architecture for a Spiking Convolutional Neural Networks (SCNNs) has been implemented in an embedded system. The aim of this implementation is to present the ability of CNNs in order to hardware utilization and power consumption in complex applications such as tumor detection. Accordingly, the structure of the proposed SCNN is deployed on an FPGA by using fixed point arithmetic. The structural variation of the brain tissue creates challenges for detection of tumors in MRI images. To evaluate the speed, accuracy, and flexibility of the proposed SCNN, Izhikevich neuron model with the spike-timing-dependent plasticity (STDP) learning rule is used. The suggested neural network is explored, considering digital implementation possibility and costs. Results of the hardware synthesis and digital implementation on a field-programmable gate array (FPGA) are presented as a proof on concept.


Brain tissue, MRI images, Spiking Neural Network, Digital Implementation, STDP

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