神经形态计算与人工智能应用的三维集成系统设计案例

Eren Kurshan, H. Li, Mingoo Seok, Yuan Xie
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引用次数: 3

摘要

在过去的十年中,人工智能在社会中找到了许多应用领域。随着人工智能解决方案变得越来越复杂,用例也越来越多,他们强调需要解决实施过程中面临的性能和能效挑战。为了应对这些挑战,人们对神经形态芯片的兴趣日益浓厚。神经形态计算依赖于非冯·诺伊曼架构以及新颖的设备、电路和制造技术来模拟人类的大脑。在这些技术中,3D集成是人工智能硬件和缩放定律延续的重要推动者。在本文中,我们概述了3D集成在神经形态芯片设计中提供的独特机会,讨论了下一代神经形态架构中出现的机会,并回顾了障碍。由于对人类大脑的功能和结构的有限理解,依赖于大脑的灵感和仿真目的的神经形态架构面临着巨大的挑战。然而,高水平的投资致力于开发神经形态芯片。我们认为,3D集成不仅为神经形态芯片的成本效益和灵活设计提供了战略优势,它还可以为整合先进功能提供设计灵活性,从而进一步促进未来的设计。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
A Case for 3D Integrated System Design for Neuromorphic Computing & AI Applications
Over the last decade, artificial intelligence has found many applications areas in the society. As AI solutions have become more sophistication and the use cases grew, they highlighted the need to address performance and energy efficiency challenges faced during the implementation process. To address these challenges, there has been growing interest in neuromorphic chips. Neuromorphic computing relies on non von Neumann architectures as well as novel devices, circuits and manufacturing technologies to mimic the human brain. Among such technologies, 3D integration is an important enabler for AI hardware and the continuation of the scaling laws. In this paper, we overview the unique opportunities 3D integration provides in neuromorphic chip design, discuss the emerging opportunities in next generation neuromorphic architectures and review the obstacles. Neuromorphic architectures, which relied on the brain for inspiration and emulation purposes, face grand challenges due to the limited understanding of the functionality and the architecture of the human brain. Yet, high-levels of investments are dedicated to develop neuromorphic chips. We argue that 3D integration not only provides strategic advantages to the cost-effective and flexible design of neuromorphic chips, it may provide design flexibility in incorporating advanced capabilities to further benefits the designs in the future.
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