Three-Dimensional Multilayer Nanomagnetic Arrays for Neuromorphic Low-Energy Magnonic Processing
用于神经形态低能磁处理的三维多层纳米磁性阵列
基本信息
- 批准号:EP/Y003276/1
- 负责人:
- 金额:$ 21.04万
- 依托单位:
- 依托单位国家:英国
- 项目类别:Research Grant
- 财政年份:2024
- 资助国家:英国
- 起止时间:2024 至 无数据
- 项目状态:未结题
- 来源:
- 关键词:
项目摘要
The energy cost of computing and artificial intelligence (AI) is spiraling out of control, forecast to reach 20.9% of global energy consumption by 2030. Training a neural net to robotically solve a Rubik's Cube toy consumed 2.8 GWh , while human brains consume just ~20 W. The recent successes of large machine learning models such as OpenAI's GPT-3 and Chat-GPT are accompanied by huge carbon footprints - Chat-GPT consumed $15 million in electricity during training & generated ~552 tons of CO2 . Its ongoing energy bill is estimated at ~$3 million/month, with accompanying levels of greenhouse emissions. This unsustainable energy consumption represents both a real barrier to reaching net-zero futures and a ceiling on the power of AI computing.A big part of this problem is that we're currently trying to do brain-like computing with computers that are nothing like a brain. Today's computers use far more energy shuttling data between separate memory and processor units than actually processing, whereas neurons in the brain provide integrated memory and processing - a key driver for their radically lower energy cost. Consequently, there is a pressing need for hardware systems that function in a brain-like (neuromorphic) manner, storing and processing information natively in the same unit.In many ways, nanomagnets behave a lot like neurons in the brain. They can react to the behaviour of surrounding magnets, flipping their poles from north to south similar to how neurons send jolts of electricity. Nanomagnets can remember what they've seen in the past and change their behaviour in response to this, learning from their experiences and gradually improving at tasks like voice recognition and pattern prediction. Nanomagnets provide both memory from their ability to remember data for 1000s of years (hard drives were originally made from nanomagnets for this reason), and processing from their ability to react nonlinearly to input data at GHz speeds - oscillating in a special way known as 'magnonics'. Indeed, the maths powering modern software neural networks originate from theoretical frameworks developed by physicists in the 1970's to describe strongly-interacting magnetic networks . The early machine learning community adopted these frameworks (originally termed Hopfield networks ) and adapted & refined them into the neural networks of today. Since the early successes of machine learning, engineers have dreamt of removing the software layer of abstraction and implementing machine learning directly in physical magnetic networks. However until recently, the engineering challenges of providing efficient data input and output schemes had prevented realisation of such systems. Our team have now solved these issues to accomplish the world-first example of neuromorphic computing in nanomagnetic arrays, using the magnon dynamics of a nanomagnetic array to process information and solve a range of AI tasks including future prediction of complex biological signals.We now have a way to massively improve the power of our AI computation at no extra energy cost, by moving our nanomagnetic arrays from 2D into 3D structures, our early results and simulations show that our computing power is likely to radically increase. In this project, we will work between a group in the UK led by early-career researcher Jack Gartside and a group in the USA lead by world-expert Prof. Benjamin Jungfleisch to test our ideas & bring low-energy, low-carbon AI one step closer to reality.
计算和人工智能(AI)的能源成本正在失控,预计到2030年将达到全球能源消耗的20.9%。训练一个神经网络来机器人解决一个魔方玩具消耗了2.8 GWh,而人类大脑只消耗了20 W。OpenAI的GPT-3和Chat-GPT等大型机器学习模型最近的成功伴随着巨大的碳足迹- Chat-GPT在训练期间消耗了1500万美元的电力,产生了约552吨二氧化碳。其持续的能源账单估计约为每月300万美元,伴随着温室气体排放水平。这种不可持续的能源消耗既代表了实现净零未来的真实的障碍,也代表了人工智能计算能力的上限。这个问题的很大一部分是,我们目前正在尝试用完全不像大脑的计算机进行类似大脑的计算。今天的计算机在单独的存储器和处理器单元之间穿梭数据所消耗的能量远远超过实际处理的能量,而大脑中的神经元提供集成的存储和处理-这是其能源成本大幅降低的关键驱动因素。因此,迫切需要一种以类似大脑(神经形态)的方式运行的硬件系统,在同一个单元中存储和处理信息。在许多方面,纳米磁体的行为很像大脑中的神经元。它们可以对周围磁铁的行为做出反应,将它们的磁极从北向南翻转,类似于神经元发送电流的方式。纳米磁体可以记住它们过去看到的东西,并改变它们的行为,从它们的经验中学习,并逐渐提高语音识别和模式预测等任务。纳米磁体提供了两种记忆,一种是它们能够记住1000年的数据(硬盘驱动器最初是由纳米磁体制成的),另一种是它们能够以GHz的速度对输入数据进行非线性反应的能力-以一种特殊的方式振荡,称为“磁振子”。事实上,现代软件神经网络的数学来源于物理学家在20世纪70年代开发的理论框架,用于描述强相互作用的磁网络。早期的机器学习社区采用了这些框架(最初称为Hopfield网络),并将其调整和改进到今天的神经网络中。自从机器学习早期取得成功以来,工程师们一直梦想着去除软件抽象层,直接在物理磁网络中实现机器学习。然而,直到最近,提供有效的数据输入和输出方案的工程挑战阻碍了这种系统的实现。我们的团队现在已经解决了这些问题,完成了世界上第一个在纳米磁阵列中进行神经形态计算的例子,使用纳米磁阵列的磁振子动力学来处理信息并解决一系列人工智能任务,包括未来复杂生物信号的预测。我们现在有一种方法可以在不需要额外能源成本的情况下大规模提高人工智能计算的能力,通过将我们的纳米磁阵列从2D移动到3D结构,我们的早期结果和模拟表明,我们的计算能力可能会大幅提高。在这个项目中,我们将与英国的一个由早期职业研究员Jack Gartside领导的小组和美国的一个由世界专家Benjamin Jungfleisch教授领导的小组合作,以测试我们的想法,并使低能耗,低碳AI更接近现实。
项目成果
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