Nanoscale Electronic Synapses Using Phase Change Devices
Nanoscale Electronic Synapses Using Phase Change Devices
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DOI：
10.1145/2463585.2463588
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发表时间：
2013-05-01
影响因子：
2.2
通讯作者：
Modha, Dharmendra S.
中科院分区：
文献类型：
--
作者：
Jackson, Bryan L.;Rajendran, Bipin;Modha, Dharmendra S.
大脑的记忆容量、计算能力、通信带宽、能量消耗和物理尺寸都倾向于随着突触数量的增加而增加，而突触数量是神经元数量的 10,000 倍。尽管使用现代数字计算机进行皮质模拟的进展很快，但经典冯·诺依曼计算机体系结构和神经系统计算结构之间的本质差异使得大规模模拟变得昂贵、耗电且耗时。在过去三十年中，基于 CMOS 的“电子皮质”神经形态实现已成为神经元行为建模的节能替代方案。然而，电子实现任何自学习系统的关键要素——可扩展至生物密度的可编程塑料赫布突触——仍然难以捉摸。我们证明了使用纳米级相变器件实现此类电子突触的可行性。我们引入了用于调节器件电导的新颖编程方案，以密切模仿生物学上观察到的尖峰时序相关可塑性（STDP）现象，并通过模拟验证这种塑性相变器件应该支持尖峰神经元网络中的简单相关学习。我们的设备以交叉阵列架构排列时，可以开发接近人脑密度（类似于每平方厘米 10(11) 个突触）和能源效率（类似于每个突触编程事件消耗 1pJ）的突触系统。
The memory capacity, computational power, communication bandwidth, energy consumption, and physical size of the brain all tend to scale with the number of synapses, which outnumber neurons by a factor of 10,000. Although progress in cortical simulations using modern digital computers has been rapid, the essential disparity between the classical von Neumann computer architecture and the computational fabric of the nervous system makes large-scale simulations expensive, power hungry, and time consuming. Over the last three decades, CMOS-based neuromorphic implementations of " electronic cortex" have emerged as an energy efficient alternative for modeling neuronal behavior. However, the key ingredient for electronic implementation of any self-learning system-programmable, plastic Hebbian synapses scalable to biological densities-has remained elusive. We demonstrate the viability of implementing such electronic synapses using nanoscale phase change devices. We introduce novel programming schemes for modulation of device conductance to closely mimic the phenomenon of Spike Timing Dependent Plasticity (STDP) observed biologically, and verify through simulations that such plastic phase change devices should support simple correlative learning in networks of spiking neurons. Our devices, when arranged in a crossbar array architecture, could enable the development of synaptronic systems that approach the density (similar to 10(11) synapses per sq cm) and energy efficiency (consuming similar to 1pJ per synaptic programming event) of the human brain.