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.
翻译:以冯纽曼建筑和古典神经网络为基础的现代人工智能系统,与大脑相比,有一些根本性的局限性。本文章讨论了这些局限性及其可以减轻的方法。接着,它概述了现有神经形态的人工智能项目,这些局限性通过将某些大脑特征纳入计算机系统的运行和组织(TrueNorth、Loihi、Tianjic、SpinNannaker、BracessusS、NeuronFlow、DYNAP、Akida)而克服了这些限制。此外,文章还介绍了根据大脑特征(神经网络、平行和无同步、信息传输的冲动性质、当地学习、垃圾、模拟和模拟计算)对神经形态的系统进行分类的原则。除了在现有硅微电子技术基础上在神经形态装置中使用新的建筑方法外,文章还讨论了使用新分子元素基的前景。还列举了最近在欧洲形态应用中使用分子方面取得的进步。