.png){kind=icon}
返回
Dynamical memristors for higher-complexity neuromorphic computing
.png){kind=icon}
PRE
AI
DOI：10.1038/s41578-022-00434-z.png)
.png){kind=icon}
原文求助.png){kind=icon}
分享.png){kind=icon}
收藏
摘要
En 中文
Research on electronic devices and materials is currently driven by both the slowing down of transistor scaling and the exponential growth of computing needs, which make present digital computing increasingly capacity-limited and power-limited. A promising alternative approach consists in performing computing based on intrinsic device dynamics, such that each device functionally replaces elaborate digital circuits, leading to adaptive 'complex computing'. Memristors are a class of devices that naturally embody higher-order dynamics through their internal electrophysical processes. In this Review, we discuss how novel material properties enable complex dynamics and define different orders of complexity in memristor devices and systems. These native complex dynamics at the device level enable new computing architectures, such as brain-inspired neuromorphic systems, which offer both high energy efficiency and high computing capacity.
Keyword:
NEURAL-NETWORKS
2ND-ORDER MEMRISTOR
MODEL
CHAOS
CLASSIFICATION
ARCHITECTURES
ALGORITHMS
DEVICES
SYSTEMS
VISION
期刊
IF:
86.2
论文数:
1.2K
被引数:
4.3W
机构
引用论文
Filament-Free Bulk Resistive Memory Enables Deterministic Analogue Switching无灯丝大容量电阻存储器可实现确定性模拟切换.png)
ADVANCED MATERIALS
OAAI
ADVANCED MATERIALSIF26.8
OAAI分享收藏Brain-inspired computing with memristors: Challenges in devices, circuits, and systems使用忆阻器的脑启发计算: 设备，电路和系统中的挑战APPLIED PHYSICS REVIEWS
APPLIED PHYSICS REVIEWSIF11.6
PREAI
分享收藏
查看更多内容