Brain-inspired computing via memory device physics
Brain-inspired computing via memory device physics
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DOI：
10.1063/5.0047641
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发表时间：
2021-05-01
期刊：
影响因子：
6.1
通讯作者：
Liu, Y.
中科院分区：
文献类型：
--
作者：
Ielmini, D.;Wang, Z.;Liu, Y.
在我们的大脑中，信息在神经元之间以尖峰的形式进行交换，其中空间(神经元激发)和时间(神经元激发时)都包含相关信息。每个神经元都通过突触与其他神经元相连，突触不断地被创建、更新和刺激，以实现信息处理和学习。在硅中实现类脑神经元/突触网络将使人工自主代理能够学习、适应并与环境相互作用。为此，基于互补金属氧化物半导体晶体管和冯·诺伊曼计算体系结构的传统微电子技术不能提供所需的能效和扩展潜力。包括电阻开关随机存取存储器(RRAM)在内的一代新兴存储器件，也被称为忆阻器，可以提供丰富的物理处理能力，包括乘法、积分、增强、抑制和时间衰减刺激，这些功能适合在电子计算机中重现人脑的一些基本现象。这项工作提供了关于大脑启发的神经形态计算设备的现状和最新更新的概述。在介绍了RRAM设备技术之后，我们讨论了人脑的主要计算功能，包括神经元整合和Fire、树突过滤以及短期和长期突触可塑性。对于这些处理功能中的每一个，我们从材料、设备结构和类脑特征方面讨论了它们的拟议实现。丰富的器件物理、纳米级的集成度、对随机变化的耐受性以及就地处理信息的能力使新兴的存储器件成为未来类脑硬件智能的一种有前途的技术。
In our brain, information is exchanged among neurons in the form of spikes where both the space (which neuron fires) and time (when the neuron fires) contain relevant information. Every neuron is connected to other neurons by synapses, which are continuously created, updated, and stimulated to enable information processing and learning. Realizing the brain-like neuron/synapse network in silicon would enable artificial autonomous agents capable of learning, adaptation, and interaction with the environment. Toward this aim, the conventional microelectronic technology, which is based on complementary metal-oxide-semiconductor transistors and the von Neumann computing architecture, does not provide the desired energy efficiency and scaling potential. A generation of emerging memory devices, including resistive switching random access memory (RRAM) also known as the memristor, can offer a wealth of physics-enabled processing capabilities, including multiplication, integration, potentiation, depression, and time-decaying stimulation, which are suitable to recreate some of the fundamental phenomena of the human brain in silico. This work provides an overview about the status and the most recent updates on brain-inspired neuromorphic computing devices. After introducing the RRAM device technologies, we discuss the main computing functionalities of the human brain, including neuron integration and fire, dendritic filtering, and short- and long-term synaptic plasticity. For each of these processing functions, we discuss their proposed implementation in terms of materials, device structure, and brain-like characteristics. The rich device physics, the nano-scale integration, the tolerance to stochastic variations, and the ability to process information in situ make the emerging memory devices a promising technology for future brain-like hardware intelligence.