光子学
冯·诺依曼建筑
计算机科学
瓶颈
内存处理
深度学习
大数据
光学计算
突触
相变存储器
神经形态工程学
计算机体系结构
人工神经网络
相变
电子工程
嵌入式系统
光电子学
材料科学
人工智能
工程类
搜索引擎
工程物理
神经科学
情报检索
生物
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作者
Harish Bhaskaran,Zengguang Cheng,Carlos Rı́os,Nathan Youngblood,C David Wright,Wolfram H. P. Pernice
摘要
The use of photonics in computing is a hot topic of interest, driven by the need for ever-increasing speed along with reduced power consumption. In existing computing architectures, photonic data storage would dramatically improve the performance by reducing latencies associated with electrical memories. At the same time, the rise of 'big data' and 'deep learning' is driving the quest for non-von Neumann and brain-inspired computing paradigms. To succeed in both aspects, we have demonstrated non-volatile multi-level photonic memory avoiding the von Neumann bottleneck in the existing computing paradigm and a photonic synapse resembling the biological synapses for brain-inspired computing using phase-change materials (Ge2Sb2Te5).
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