基于FPGA的快速双比率尖端集成网络用于放射性同位素识别
Shouyu Xie1, Edward Jones2, Siru Zhang3
1University of Edinburgh, Alexander Crum Brown Road, Kings Buildings, Edinburgh, EH9 3FF, United Kingdom.
概括
这项研究介绍了一种高效的FPGA,用于放射性同位素识别的尖端神经网络 (SNN) 的实现. 优化的网络实现了高精度与低功耗,使其适合于现实世界的应用.
科学领域:
- 人工智能的人工智能
- 核科学与工程 核科学与工程
- 计算机工程 计算机工程
背景情况:
- 尖端神经网络 (SNN) 提供节能计算.
- 放射性同位素识别对于核安全和核研究至关重要.
- FPGA实现允许人工智能模型的硬件加速.
研究的目的:
- 为放射性同位素识别开发和实施基于FPGA的双比数组合SNN.
- 为了优化SNN的高精度和低功耗.
- 在精度,速度和能源效率方面评估实施的SNN的性能.
主要方法:
- 集体SNN的训练和转换,包括20个三层网络和1160个神经元.
- 在网络压缩中应用已学习的步骤量化 (LSQ) 和修剪技术.
- 在100MHz时钟频率的Artix-7 FPGA板上实现.
主要成果:
- 在放射性同位素识别中获得了97.04%的准确性,准确性损失不到1%.
- 网络参数压缩缩小大小到原来的30%.
- 每个样本的推断时间为334μs.
- 每个推断估计耗电量为157μJ.
结论:
- 开发的基于FPGA的SNN证明了放射性同位素识别的可行和有效的解决方案.
- LSQ和修剪是优化硬件部署SNN的有效技术.
- 该实施展示了SNN在核应用中低功耗,高精度边缘计算的潜力.
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