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

• 批准号: EP/Y003276/1
• 负责人: Jack C. Gartside
• 金额: $ 21.04万
• 依托单位: Imperial College London
• 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: https://gtr.ukri.org/projects?ref=EP%2FY003276%2F1
• 关键词: Three; Dimensional; Multilayer; Nanomagnetic; Arrays

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 Nanomagnets provide both memory from their ability to remember data for 1000s of years (hard drives were originally made from nanomagnets for this reason), and processing from their ability to react nonlinearly to input data at GHz speeds - oscillating in a special way known as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年到2030年达到全球能源消耗的20.9％。训练神经网以机器人解决2.8 gwh的魔方摄取的魔方2.8 gwh，而人类的大脑却刚刚消耗了〜20 w。培训期间的1500万美元电力，并产生了约552吨二氧化碳。其持续的能源法案估计为每月300万美元，随附的温室排放水平。这种不可持续的能源消耗既代表了达到零净期货的真正障碍，又代表了AI计算能力的上限。这个问题的重要部分是，我们目前正在尝试使用与大脑不同的计算机进行类似大脑的计算。当今的计算机在单独的内存和处理器单元之间使用更多的能量穿梭数据，而不是实际处理，而大脑中的神经元则提供了集成的内存和处理 - 这是其从根本上降低能量成本的关键驱动力。因此，对以脑状（神经形态）方式起作用的硬件系统迫切需要，在同一单元中存储和处理信息。在许多方面，纳米磁体的行为与大脑中的神经元相似。他们可以对周围磁铁的行为做出反应，从北向南翻转两极，类似于神经元如何发出电力。纳米磁体可以记住他们过去所看到的，并为此改变其行为，从他们的经验中学习，并在语音识别和模式预测等任务上逐渐改进。纳米磁铁从记忆的能力中提供了1000年的记忆能力（由于这个原因，硬驱动器最初是由纳米磁铁制成的），以及从他们在GHz速度下以非线性反应对输入数据反应的能力 - 以一种特殊的方式振荡，以一种被称为“ Magnonics”的方式进行振动。确实，为现代软件神经网络提供动力的数学源于1970年代物理学家开发的理论框架，以描述强烈相互交互的磁网络。早期的机器学习社区采用了这些框架（最初称为Hopfield Networks），并将其改编成当今的神经网络。自从机器学习的早期成功以来，工程师梦想着直接在物理磁网络中直接删除抽象的软件层并实现机器学习。但是直到最近，提供有效的数据输入和输出方案的工程挑战仍阻止了这种系统的实现。我们的团队现在已经解决了这些问题，以实现纳米磁性阵列中神经形态计算的第一个示例，利用纳米磁阵列的镁动力学来处理信息并解决一系列AI任务，包括未来对复杂生物学信号的未来预测。我们现在可以通过8个额外的能量来提高我们的AI额定能力，以使我们的AI额定能力提高我们的AI，我们的AI量可以使我们的AI驱动器的能力，这是我们的AI型号，使我们的AI驱动良好。结构，我们的早期结果和模拟表明，我们的计算能力可能会从根本上增加。在这个项目中，我们将在英国的一个小组之间由早期研究员杰克·加特赛德（Jack Gartside）领导的团体和由世界专家Benjamin Jungfleisch领导的美国团体，以测试我们的想法并带来低能量，低碳AI，一步很近。