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研究团队展示了世界上最快的光学神经形态处理器

研究团队展示了世界上最快的光学神经形态处理器

Research team demonstrates world's fastest optical neuromorphic processor

研究团队展示了世界上最快的光学神经形态处理器


by Swinburne University of Technology

斯威本科技大学

 

Xu博士具有集成的光学微梳芯片,该芯片构成了光学神经形态处理器的核心部分.png


Dr Xingyuan (Mike) Xu with the integrated optical microcomb chip, which forms the core part of the optical neuromorphic processor. Credit: Swinburne University of Technology

XingyuanMikeXu博士具有集成的光学微梳芯片,该芯片构成了光学神经形态处理器的核心部分。图片来源:斯威本科技大学


An international team of researchers led by Swinburne University of Technology has demonstrated the world's fastest and most powerful optical neuromorphic processor for artificial intelligence (AI), which operates faster than 10 trillion operations per second (TeraOPs/s) and is capable of processing ultra-large scale data. Published in the prestigious journal Nature, this breakthrough represents an enormous leap forward for neural networks and neuromorphic processing in general.

由斯威本科技大学领导的国际研究人员团队展示了世界上最快,最强大的人工智能光学神经形态处理器(AI),其运算速度超过每秒10万亿次运算(TeraOPs / s),并能够处理超大规模数据。这一突破发表在著名的《自然》杂志上,代表了神经网络和整个神经形态处理技术的巨大飞跃。


Artificial neural networks, a key form of AI, can 'learn' and perform complex operations with wide applications to computer vision, natural language processing, facial recognition, speech translation, playing strategy games, medical diagnosis and many other areas. Inspired by the biological structure of the brain's visual cortex system, artificial neural networks extract key features of raw data to predict properties and behavior with unprecedented accuracy and simplicity.

人工神经网络是AI的一种重要形式,可以学习并执行复杂的操作,并广泛应用于计算机视觉,自然语言处理,面部识别,语音翻译,玩策略游戏,医疗诊断和许多其他领域。受大脑视觉皮层系统生物结构的启发,人工神经网络提取原始数据的关键特征,以前所未有的准确性和简单性预测属性和行为。


Led by Swinburne's Professor David Moss, Dr. Xingyuan (Mike) Xu (Swinburne, Monash University) and Distinguished Professor Arnan Mitchell from RMIT University, the team achieved an exceptional feat in optical neural networks: dramatically accelerating their computing speed and processing power.

Swinburne教授David MossXingyuanMikeXu博士(莫纳什大学Swinburne)和RMIT大学杰出教授Arnan Mitchell的带领下,该团队在光学神经网络领域取得了非凡的成就:极大地提高了它们的计算速度和处理能力。


The team demonstrated an optical neuromorphic processor operating more than 1000 times faster than any previous processor, with the system also processing record-sized ultra-large scale images—enough to achieve full facial image recognition, something that other optical processors have been unable to accomplish.

该团队演示了一种光学神经形态处理器,其运行速度比任何以前的处理器快1000倍以上,并且该系统还可以处理创纪录大小的超大型图像-足以实现全脸部图像识别,这是其他光学处理器无法做到的。


"This breakthrough was achieved with 'optical micro-combs," as was our world-record internet data speed reported in May 2020," says Professor Moss, Director of Swinburne's Optical Sciences Centre.

Swinburne光学科学中心主任Moss教授说:这一突破是通过'光学微梳子实现的,正如我们在20205月报道的世界记录互联网数据速度一样。


While state-of-the-art electronic processors such as the Google TPU can operate beyond 100 TeraOPs/s, this is done with tens of thousands of parallel processors. In contrast, the optical system demonstrated by the team uses a single processor and was achieved using a new technique of simultaneously interleaving the data in time, wavelength and spatial dimensions through an integrated micro-comb source.

虽然最先进的电子处理器(例如Google TPU)可以超过100 TeraOPs / s的速度运行,但这可以通过数以万计的并行处理器来完成。相反,该团队演示的光学系统使用单个处理器,并且是通过一种新技术实现的,该技术通过集成的微梳状光源同时在时间,波长和空间维度上对数据进行交织。


Micro-combs are relatively new devices that act like a rainbow made up of hundreds of high- quality infrared lasers on a single chip. They are much faster, smaller, lighter and cheaper than any other optical source.

微梳子是相对较新的设备,其作用就像彩虹一样,它由单个芯片上的数百个高质量红外激光组成。它们比任何其他光源都更快,更小,更轻且更便宜。


"In the 10 years since I co-invented them, integrated micro-comb chips have become enormously important and it is truly exciting to see them enabling these huge advances in information communication and processing. Micro-combs offer enormous promise for us to meet the world's insatiable need for information," says Professor Moss.

自从我共同发明它们以来的10年中,集成的微梳状芯片已经变得非常重要,看到它们在信息通信和处理方面实现这些巨大的进步,这真是令人兴奋。微梳子为我们满足这些挑战提供了巨大的希望。世界对信息的无限需求。莫斯教授说。


"This processor can serve as a universal ultrahigh bandwidth front end for any neuromorphic hardware —optical or electronic based—bringing massive-data machine learning for real-time ultrahigh bandwidth data within reach," says co-lead author of the study, Dr. Xu, Swinburne alum and postdoctoral fellow with the Electrical and Computer Systems Engineering Department at Monash University.

研究的共同主要作者说:该处理器可以用作任何神经形态硬件(基于光学或电子的)的通用超高带宽前端,为海量实时实时超高带宽数据带来海量数据机器学习。徐,斯威本的明矾和莫纳什大学电气与计算机系统工程系的博士后研究员。


"We're currently getting a sneak-peak of how the processors of the future will look. It's really showing us how dramatically we can scale the power of our processors through the innovative use of microcombs," Dr. Xu explains.

徐博士解释说:我们目前正在对未来的处理器的外观进行一个简单的介绍。这确实向我们展示了如何通过创新性地使用微梳来极大地扩展处理器的功能。


RMIT's Professor Mitchell adds, "This technology is applicable to all forms of processing and communications—it will have a huge impact. Long term we hope to realize fully integrated systems on a chip, greatly reducing cost and energy consumption."

RMITMitchell教授补充说:这项技术适用于所有形式的处理和通信,它将产生巨大的影响。从长远来看,我们希望在芯片上实现完全集成的系统,从而大大降低成本和能耗。


"Convolutional neural networks have been central to the artificial intelligence revolution, but existing silicon technology increasingly presents a bottleneck in processing speed and energy efficiency," says key supporter of the research team, Professor Damien Hicks, from Swinburne and the Walter and Elizabeth Hall Institute.

卷积神经网络一直是人工智能革命的核心,但是现有的硅技术日益成为处理速度和能源效率的瓶颈,研究团队的主要支持者,来自SwinburneDamien Hicks教授以及沃尔特和伊丽莎白·霍尔研究所。


He adds, "This breakthrough shows how a new optical technology makes such networks faster and more efficient and is a profound demonstration of the benefits of cross-disciplinary thinking, in having the inspiration and courage to take an idea from one field and using it to solve a fundamental problem in another."

他补充说:这一突破表明,新的光学技术如何使此类网络更快,更高效,并且深刻展示了跨学科思维的好处,它具有启发和勇气,可以从一个领域汲取灵感并将其运用到解决另一个根本问题。


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来源于: techXplore

 


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