Non-Boolean computing with nanomagnets for computer vision applications
Non-Boolean computing with nanomagnets for computer vision applications
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DOI：
10.1038/nnano.2015.245
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发表时间：
2016-02-01
影响因子：
38.3
通讯作者：
Sarkar, Sudeep
中科院分区：
文献类型：
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作者：
Bhanja, Sanjukta;Karunaratne, D. K.;Sarkar, Sudeep
纳米磁性领域最近引起了极大的关注，因为它有可能提供低功耗、高速和密集的非易失性存储器。现在可以设计亚100nm磁性结构的尺寸、形状、间距、方向和组成。这激发了对纳米磁体用于非常规计算范例的探索。在这里，我们利用纳米磁系统的能量最小化特性来解决计算机视觉应用中出现的二次优化问题，这些问题的计算成本很高。通过利用纳米磁盘的磁化状态作为涡旋和单畴的状态表示，我们开发了一个磁哈密顿量，并将其实现在一个磁系统中，该系统可以识别给定图像的显著特征，其真阳性率超过85%。这些结果表明，这种替代计算方法开发磁性协处理器的潜力，可能在比传统处理器更少的时钟周期内解决复杂问题。
The field of nanomagnetism has recently attracted tremendous attention as it can potentially deliver low-power, highspeed and dense non-volatile memories. It is now possible to engineer the size, shape, spacing, orientation and composition of sub-100 nm magnetic structures. This has spurred the exploration of nanomagnets for unconventional computing paradigms. Here, we harness the energy-minimization nature of nanomagnetic systems to solve the quadratic optimization problems that arise in computer vision applications, which are computationally expensive. By exploiting the magnetization states of nanomagnetic disks as state representations of a vortex and single domain, we develop a magnetic Hamiltonian and implement it in a magnetic system that can identify the salient features of a given image with more than 85% true positive rate. These results show the potential of this alternative computing method to develop a magnetic coprocessor that might solve complex problems in fewer clock cycles than traditional processors.