论文标题
使用FPGA的基于尖峰神经网络的低功率放射性同位素识别
Spiking Neural Network Based Low-Power Radioisotope Identification using FPGA
论文作者
论文摘要
本文介绍了基于放射性同位素识别的基于尖峰神经网络（SNN）的低功率设计的详细方法。在FPGA上已经达到了72 MW的低功率成本，在10 cm测试距离时的推理精度为100％，在25 cm时的推理精度为97％。提出了设计验证和芯片验证方法。它还讨论了在大三角帆上的SNN模拟，以进行快速原型制作以及应用程序特定的各种考虑因素，例如测试距离，集成时间和SNN超参数选择。
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.
