Stochastic memristive devices for computing and neuromorphic applications
Stochastic memristive devices for computing and neuromorphic applications
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DOI：
10.1039/c3nr01176c
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发表时间：
2013-01-01
期刊：
影响因子：
6.7
通讯作者：
Lu, Wei
中科院分区：
文献类型：
--
作者：
Gaba, Siddharth;Sheridan, Patrick;Lu, Wei
纳米级电阻开关器件(记忆器件或忆阻器)已被研究用于从非易失性存储器、逻辑到神经形态系统的许多应用。然而，一个主要的挑战是解决这些纳米设备在空间和时间上潜在的巨大变化。在这里，我们证明了在金属丝基记忆器件中，开关可以是完全随机的。虽然单个切换事件是随机的，但切换的分布和概率可以很好地预测和控制。阻性开关的内在随机性允许这些二进制器件被用作新的容错计算方案(例如随机计算)的构建块，并为神经形态应用提供所需的“模拟”特征，而不是试图使用过高的电压或时间来强制高开关概率。为了验证这种潜力，我们展示了时间域和空间域中基于忆阻器的随机比特流，并展示了二进制忆阻器阵列可以作为神经形态应用的多电平“模拟”装置。
Nanoscale resistive switching devices (memristive devices or memristors) have been studied for a number of applications ranging from non-volatile memory, logic to neuromorphic systems. However a major challenge is to address the potentially large variations in space and time in these nanoscale devices. Here we show that in metal-filament based memristive devices the switching can be fully stochastic. While individual switching events are random, the distribution and probability of switching can be well predicted and controlled. Rather than trying to force high switching probabilities using excess voltage or time, the inherent stochastic nature of resistive switching allows these binary devices to be used as building blocks for novel error-tolerant computing schemes such as stochastic computing and provides the needed "analog" feature for neuromorphic applications. To verify such potential, we demonstrated memristor-based stochastic bitstreams in both time and space domains, and show that an array of binary memristors can act as a multi-level "analog" device for neuromorphic applications.