在基于人口编码的计算系统中,通过持续学习克服设备不可靠性
Alice Mizrahi1,2, Julie Grollier3, Damien Querlioz4
1National Institute of Standards and Technology, Gaithersburg, USA.
Journal of applied physics
|October 25, 2024
概括
灵感来自于大脑的大脑.
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:
- 大脑通过冗余和持续学习表现出对组件不可靠性的强度.
- 从不可靠的纳米设备构建可靠的计算系统是一个重大挑战.
研究的目的:
- 为了演示使用人口编码和磁道连接的脑启发的计算系统.
- 调查持续学习在提高系统稳定性和使不可靠组件的使用成为可能方面的作用.
主要方法:
- 开发了一个利用人口编码的计算系统,用于神经元和突触权重的磁道连接.
- 实施了持续学习算法,以实现组件故障的适应和恢复.
- 分析了功耗,精度和内存特征之间的权衡.
主要成果:
- 该系统通过持续学习证明了从神经元损失中恢复.
- 成功地利用了不可靠的突触权重,特别是低能量的屏障磁性记忆.
- 确定了神经元数量和重量能量屏障之间的最佳平衡,以在给定的精度下最大限度地降低功耗.
结论:
- 具有持续学习的脑启发架构为强大的神经形态计算提供了可行的途径.
- 在这种系统中,使用不可靠的磁性内存是可行的,只要仔细优化.
- 实现低功耗,高精度计算需要平衡系统参数,如神经元数量和突触性质.
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