Computing with networks of nonlinear mechanical oscillators.
Computing with networks of nonlinear mechanical oscillators.
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DOI：
10.1371/journal.pone.0178663
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发表时间：
2017
期刊：
影响因子：
3.7
通讯作者：
Sylvestre J
中科院分区：
文献类型：
--
作者：
Coulombe JC;York MCA;Sylvestre J
由于光刻技术达到了基本的物理极限，微电子计算设备的密度和功率效率越来越难实现，因此需要新的方法来最大限度地利用分布式传感器、微型机器人或智能材料。受生物启发的设备，如人工神经网络，可以高度并行地处理信息，有效地解决难题，即使使用传统的微电子技术也是如此。我们描述了一种机械设备，其操作方式类似于人工神经网络，以有效地解决两个困难的基准问题(计算比特流的奇偶性和对语音单词进行分类)。该器件由一个由线性弹簧连接的质量网络组成，并通过非线性弹簧连接到衬底上，从而形成一个非简谐振荡器网络。由于质量可以直接耦合到施加在设备上的力，这种方法将传感和计算功能结合在一个尺寸紧凑的高能效设备中。
As it is getting increasingly difficult to achieve gains in the density and power efficiency of microelectronic computing devices because of lithographic techniques reaching fundamental physical limits, new approaches are required to maximize the benefits of distributed sensors, micro-robots or smart materials. Biologically-inspired devices, such as artificial neural networks, can process information with a high level of parallelism to efficiently solve difficult problems, even when implemented using conventional microelectronic technologies. We describe a mechanical device, which operates in a manner similar to artificial neural networks, to solve efficiently two difficult benchmark problems (computing the parity of a bit stream, and classifying spoken words). The device consists in a network of masses coupled by linear springs and attached to a substrate by non-linear springs, thus forming a network of anharmonic oscillators. As the masses can directly couple to forces applied on the device, this approach combines sensing and computing functions in a single power-efficient device with compact dimensions.