  this paper presents a detailed methodology of a Spiking Neural Network (SNN)
based low-power design for radioisotope identification. A low power cost of 72
mW has been achieved on FPGA with the inference accuracy of 100% at 10 cm test
distance and 97% at 25 cm. The design verification and chip validation methods
are presented. It also discusses SNN simulation on SpiNNaker for rapid
prototyping and various considerations specific to the application such as test
distance, integration time, and SNN hyperparameter selections.
