论文标题
神经形态人工智能系统
Neuromorphic Artificial Intelligence Systems
论文作者
论文摘要
与大脑相比，基于von Neumann架构和经典神经网络的现代AI系统具有许多基本局限性。本文讨论了这种局限性及其可以缓解的局限性。接下来，它概述了当前可用的神经形态AI项目，其中通过将一些大脑功能带入计算系统的功能和组织（Truenorth，Loihi，Tianjic，Tianjic，Spinnaker，Brainscales，brainscales，nearuronflow，dynap，akida，akida），从而克服了这些限制。此外，本文介绍了通过使用的大脑特征（神经网络，平行性和异步性，信息传递的脉冲性质，局部学习，稀疏，模拟和内存计算）对神经形态AI系统进行分类的原则。除了基于现有的硅微电子技术中的神经形态设备中使用的新建筑方法外，该文章还讨论了使用新的Memristor元素基础的前景。还给出了在欧洲形态应用中使用备忘录的最新进展的示例。
Modern AI systems, based on von Neumann architecture and classical neural networks, have a number of fundamental limitations in comparison with the brain. This article discusses such limitations and the ways they can be mitigated. Next, it presents an overview of currently available neuromorphic AI projects in which these limitations are overcame by bringing some brain features into the functioning and organization of computing systems (TrueNorth, Loihi, Tianjic, SpiNNaker, BrainScaleS, NeuronFlow, DYNAP, Akida). Also, the article presents the principle of classifying neuromorphic AI systems by the brain features they use (neural networks, parallelism and asynchrony, impulse nature of information transfer, local learning, sparsity, analog and in-memory computing). In addition to new architectural approaches used in neuromorphic devices based on existing silicon microelectronics technologies, the article also discusses the prospects of using new memristor element base. Examples of recent advances in the use of memristors in euromorphic applications are also given.
