Coulombe Jean C, York Mark C A, Sylvestre Julien
Department of Mechanical Engineering, Université de Sherbrooke, Sherbrooke, Canada.
PLoS One. 2017 Jun 2;12(6):e0178663. doi: 10.1371/journal.pone.0178663. eCollection 2017.
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.
由于光刻技术已达到基本物理极限，要提高微电子计算设备的密度和功率效率变得越来越困难，因此需要新的方法来最大化分布式传感器、微型机器人或智能材料的优势。受生物启发的设备，如人工神经网络，即使采用传统微电子技术实现，也能以高度并行的方式处理信息，从而高效解决难题。我们描述了一种机械设备，其运行方式类似于人工神经网络，能有效解决两个困难的基准问题（计算比特流的奇偶性和对语音进行分类）。该设备由通过线性弹簧耦合的质量块网络组成，并通过非线性弹簧连接到基板上，从而形成一个非谐振荡器网络。由于质量块可以直接耦合到施加在设备上的力，这种方法在一个尺寸紧凑、功率高效的设备中结合了传感和计算功能。
