Abstract:
Spiking Neural Network (SNN) is very popular and effective in modelling the physical neurons compared to other models of
the neural network. Besides the software implementation of the neuromorphic processors, hardware implementation of the neuromorphic
processors is also very important in order to apply it in real-time domain. In this work, a hardware efficient architecture of the
neuromorphic processor is proposed. The proposed architecture is efficient in terms of low usage of memory elements and other
hardware resources. Virtex-6 field programmable gate array (FPGA) development board is used to validate the proposed design. Fixed
data format of width 18 is used in this work and 10-bit is reserved for the fractional part. The proposed architecture is applied to detect
the handwritten digits. In this work, MNIST database is used to train and validate the SNN. The proposed architecture achieves 90%
accuracy when used to recognize the handwritten digit data.
