利用百万规模的非冯诺依曼计算加速机器学习和高性能计算

L. Daudet
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引用次数: 0

摘要

当前的大规模计算,例如在高性能计算或大规模机器学习模型的训练中,经常受到“内存瓶颈”的困扰,特别是在处理高维数据时。在这里,我们提出了一种新的非冯·诺伊曼光子硬件,利用多重光散射。光学处理单元可以无缝集成到各种混合光子/硅管道中,实现最先进的机器学习或高性能计算算法。它们为新一代大规模计算提供了一条可靠的途径,既可扩展又可持续。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Leveraging million-scale Non von Neumann computations for accelerated Machine Learning and High Performance Computing
Current large-scale computations, for instance in High Performance Computing or in the training of massive Machine Learning models, often suffer from the “memory bottleneck”, especially when dealing with high-dimensional data. Here, we present a new non-von Neumann photonic hardware, leveraging multiple light scattering. Optical Processing Units can be seamlessly integrated into a variety of hybrid photonics / silicon pipelines implementing state-of-the-art Machine Learning or High Performance Computing algorithms. They offer a credible pathway towards a new generation of large-scale computing, both scalable and sustainable.
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