论文标题
节能神经形态计算的量子材料
Quantum materials for energy-efficient neuromorphic computing
论文作者
论文摘要
随着我们满足有效处理大量数据的未来需求，神经形态计算方法变得越来越重要。量子材料的独特属性可以通过在硬件级别实现神经形态思想的新节能设备概念来帮助满足这些需求。特别是，强相关性产生高度非线性响应，例如可以利用短期和长期可塑性来利用的导电相变。同样，磁化动力学是非常非线性的，可以用于数据分类。本文讨论了这些方法的精选示例，并为当前的机遇和挑战提供了将基于量子材料的设备组装为神经形态功能的设备，以进入更大的新兴复杂网络系统。
Neuromorphic computing approaches become increasingly important as we address future needs for efficiently processing massive amounts of data. The unique attributes of quantum materials can help address these needs by enabling new energy-efficient device concepts that implement neuromorphic ideas at the hardware level. In particular, strong correlations give rise to highly non-linear responses, such as conductive phase transitions that can be harnessed for short and long-term plasticity. Similarly, magnetization dynamics are strongly non-linear and can be utilized for data classification. This paper discusses select examples of these approaches, and provides a perspective for the current opportunities and challenges for assembling quantum-material-based devices for neuromorphic functionalities into larger emergent complex network systems.
