论文标题
使用Fowler-Nordheim量子隧道进行装置的突触记忆合并
On-device Synaptic Memory Consolidation using Fowler-Nordheim Quantum-tunneling
论文作者
论文摘要
突触记忆巩固已被认为是支持神经形态人工智能（AI）系统中持续学习的关键机制之一。在这里，我们报告说，Fowler-Nordheim（FN）量子驾驶装置可以实现突触存储器巩固，类似于算法合并模型（如级联和弹性重量合并（EWC）模型）所能实现的。拟议的FN-Synapse不仅存储突触重量，而且还存储了Synapse在设备本身上的历史用法统计量。我们还表明，就突触寿命而言，FN合并的操作几乎是最理想的，并且我们证明了一个包含FN合成的网络在小基准持续学习任务中胜过可比的EWC网络。借助Femtojoules的能源足迹，我们相信所提出的FN-Synapse为实施突触记忆巩固和持续学习提供了一种超能量的方法。
Synaptic memory consolidation has been heralded as one of the key mechanisms for supporting continual learning in neuromorphic Artificial Intelligence (AI) systems. Here we report that a Fowler-Nordheim (FN) quantum-tunneling device can implement synaptic memory consolidation similar to what can be achieved by algorithmic consolidation models like the cascade and the elastic weight consolidation (EWC) models. The proposed FN-synapse not only stores the synaptic weight but also stores the synapse's historical usage statistic on the device itself. We also show that the operation of the FN-synapse is near-optimal in terms of the synaptic lifetime and we demonstrate that a network comprising FN-synapses outperforms a comparable EWC network for a small benchmark continual learning task. With an energy footprint of femtojoules per synaptic update, we believe that the proposed FN-synapse provides an ultra-energy-efficient approach for implementing both synaptic memory consolidation and persistent learning.
