使用贝叶斯神经网络进行容错神经形态计算的设计方法
Di Gao1, Xiaoru Xie2, Dongxu Wei3
1The School of Intelligent Manufacturing, Hangzhou Polytechnic, Hangzhou 311402, China.
Micromachines
|October 28, 2023
概括
这项研究引入了贝叶斯神经网络方法,以改善使用memristor交叉条数组的神经形态计算. 它考虑了设备的变化,以确保可靠的推断性能,提高故障容忍度.
科学领域:
- 神经形态计算是一种神经形态计算.
- 材料科学是一种材料科学.
- 人工智能的人工智能是人工智能.
背景情况:
- 记忆器交叉条数组是神经形态计算的关键.
- 过程变化导致随机权重分布,影响推理准确度.
- 准确的重量分布学习对于可靠的基于memristor的系统至关重要.
研究的目的:
- 使用memristor交叉条数组开发用于神经形态计算的容错设计方法.
- 为了解决由memristor过程变化引起的推断性能退化.
- 为了提高神经形态系统对设备不确定性的稳定性.
主要方法:
- 使用贝叶斯神经网络框架.
- 结合变量贝叶斯推理与故障感知变量后部分布.
- 将memristor偏差纳入算法训练中,以优化重量分布.
主要成果:
- 提出的贝叶斯推理框架成功地将memristor变异集成到训练中.
- 优化的权重分布适应了不确定性,并最大限度地降低了推理退化.
- 实验结果显示了对处理变化和噪声的耐受性,确保了可靠的计算.
结论:
- 开发的方法使得能够在memristor交叉条数组中实现容错的神经形态计算.
- 贝叶斯神经网络有效地减轻了由于memristor变化的推断性能损失.
- 这种方法提高了下一代计算硬件的可靠性和稳定性.
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