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On-chip phase-change photonic memory and computing

机译:片上相变光子存储器和计算

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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 (Ge_2Sb_2Te_5).
机译:在不断提高的速度和降低的功耗需求的推动下,光子学在计算中的使用成为人们关注的热点。在现有的计算架构中,光子数据存储将通过减少与电存储器相关的延迟来显着提高性能。同时,“大数据”和“深度学习”的兴起推动了对非冯·诺依曼和脑启发式计算范例的追求。为了在这两个方面都取得成功,我们展示了非易失性多级光子存储,避免了现有计算范式中的冯·诺依曼瓶颈,并且光子突触类似于使用相变材料(Ge_2Sb_2Te_5)进行脑启发式计算的生物突触。

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