神经形态工程学
冯·诺依曼建筑
材料科学
计算机科学
能源消耗
计算
电阻随机存取存储器
能量(信号处理)
氧化物
GSM演进的增强数据速率
光电子学
人工神经网络
电子工程
人工智能
电气工程
电压
算法
工程类
物理
冶金
操作系统
量子力学
作者
Shimeng Yu,Bin Gao,Zheng Fang,Hongyu Yu,Jinfeng Kang,H.-S. Philip Wong
标识
DOI:10.1002/adma.201203680
摘要
Neuromorphic computing is an emerging computing paradigm beyond the conventional digital von Neumann computation. An oxide-based resistive switching memory is engineered to emulate synaptic devices. At the device level, the gradual resistance modulation is characterized by hundreds of identical pulses, achieving a low energy consumption of less than 1 pJ per spike. Furthermore, a stochastic compact model is developed to quantify the device switching dynamics and variation. At system level, the performance of an artificial visual system on the image orientation or edge detection with 16 348 oxide-based synaptic devices is simulated, successfully demonstrating a key feature of neuromorphic computing: tolerance to device variation.
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