用电阻开关电子突触训练概率图模型
Training a Probabilistic Graphical Model with Resistive Switching Electronic Synapses
摘要：
当前深度学习和数据采掘的大规模实现需要数千个处理器、大量的片外存储器，并且消耗千兆焦耳的能量。新兴的存储器技术，如纳米级双端子电阻开关存储器器件提供了一种紧凑、可扩展和低功耗的替代方案，允许在细粒度分布式并行架构中进行片上协同定位处理和存储。在这里，我们报告了第一次使用阻变存储器器件来实现和训练受限玻尔兹曼机（RBM），这是一种生成概率图形模型，作为深度网络中无监督学习的关键组件。我们实验证明了一个45突触RBM实现90电阻开关相变存储器（PCM）的元素与生物启发的变体的对比发散（CD）算法训练，实现赫布和反赫布的权重更新。与未经训练的情况相比，电阻式PCM设备在经过30个时期训练的缺失像素图案完成任务中显示出出错率的两倍到十倍的降低。测量的编程能耗为6.1 nJ每历元的电阻开关PCM设备，一个因素的约150倍，低于传统的处理器存储器系统。我们分析和讨论的学习性能上的周期到周期的变化，以及在PCM模拟存储器设备的渐进的水平的数量的依赖。
英文摘要：
Current large scale implementations of deep learning and data mining require thousands of processors, massive amounts of off-chip memory, and consume gigajoules of energy. Emerging memory technologies such as nanoscale two-terminal resistive switching memory devices offer a compact, scalable and low power alternative that permits on-chip co-located processing and memory in fine-grain distributed parallel architecture. Here we report first use of resistive switching memory devices for implementing and training a Restricted Boltzmann Machine (RBM), a generative probabilistic graphical model as a key component for unsupervised learning in deep networks. We experimentally demonstrate a 45-synapse RBM realized with 90 resistive switching phase change memory (PCM) elements trained with a bio-inspired variant of the Contrastive Divergence (CD) algorithm, implementing Hebbian and anti-Hebbian weight updates. The resistive PCM devices show a two-fold to ten-fold reduction in error rate in a missing pixel pattern completion task trained over 30 epochs, compared to untrained case. Measured programming energy consumption is 6.1 nJ per epoch with the resistive switching PCM devices, a factor of ~150 times lower than conventional processor-memory systems. We analyze and discuss the dependence of learning performance on cycle-to-cycle variations as well as number of gradual levels in the PCM analog memory devices.
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