RCN-SC: Research Coordination Network for Design and Testing of Neuromorphic Integrated Circuits

该项目将促进神经形态集成电路教育（NICE）研究协调网络（RCN）的发展，为对神经形态集成电路（ic）的设计、制造和测试感兴趣的学生和研究人员降低进入门槛。随着人工智能（AI）不断渗透社会，对高效人工智能硬件、加速器和集成电路（ic）的需求不断增加。神经形态工程领域在推进人工智能硬件方面发挥了重要作用，将内存计算、池化和注意力等概念集成到主流IC设计中。然而，为了维持该领域的增长，迫切需要解决集成电路设计和制造学科的教育和劳动力差距。通过利用人工智能、机器学习和神经形态工程学科的普及，该项目正在激励研究人员获得设计技能，然后应用于集成电路设计的各个领域。本次RCN的核心是年度碲化神经形态认知工程（TNCE）研讨会，这是神经科学和人工智能领域的一个有影响力和备受期待的事件。本次研讨会汇集了来自不同学科的领先研究人员、专家和学生，探索和推进我们对神经形态计算和认知的理解。作为RCN的一部分，该项目利用研讨会基础设施来组织讨论小组，促进实践培训活动，并围绕神经形态集成电路的主题形成可持续的研究队列。RCN的智力优势在于理解和利用神经形态设备和硬件中的突发神经动力学和噪声诱导过程，以有效地解决特定的计算任务。为了实现这一目标，RCN正在利用网络内的广泛专业知识来研究新的神经形态架构、电路和硬件，与使用中央处理单元（cpu）、图形处理单元（gpu）或量子处理器的其他计算架构相比，这些架构可以带来显著的性能优势。RCN由三个协调子网络组成：(a) NICE-RCN协同设计框架侧重于行为模型和软件的开发。这些工具将能够在基准任务上模拟和评估不同的架构；(b) NICE-RCN IC设计框架将专注于使用可通过Efabless访问的开源IC制造工具以及Muse半导体提供的台积电（TSMC） PDK工具设计神经形态工艺设计套件（PDK）；(c) NICE-RCN集成电路测试框架将建立测试基础设施并创建评估神经形态集成电路性能的基准。该奖项反映了美国国家科学基金会的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。

The project is facilitating a Neuromorphic Integrated Circuits Education (NICE) research coordination network (RCN) that will lower the barrier of entry for students and researchers interested in the design, fabrication and testing of neuromorphic integrated circuits (ICs). As artificial intelligence (AI) continues to permeate society, there has been an ever-increasing demand for efficient AI hardware, accelerators and integrated circuits (ICs). The field of neuromorphic engineering has been instrumental in advancing AI hardware, with concepts like compute-in-memory, pooling, and attention being integrated into mainstream IC designs. However, to sustain the growth of the field, there is a pressing need to address the education and workforce gap in the discipline of IC design and fabrication. By leveraging the popularity of AI, machine learning and neuromorphic engineering disciplines, the project is motivating researchers to acquire design skills that can then be applied to various domains of IC design. At the centerpiece of this RCN is the Annual Telluride Neuromorphic Cognition Engineering (TNCE) workshop which is an influential and highly anticipated event in the field of neuroscience and artificial intelligence. This workshop brings together leading researchers, experts, and students from various disciplines to explore and advance our understanding of neuromorphic computing and cognition. As a part of this RCN, the project is leveraging the workshop infrastructure to organize discussion groups, facilitate hands-on training events, and form sustainable research cohorts around the theme of neuromorphic ICs.The intellectual merit of the RCN is to understand and exploit emergent neurodynamics and noise-induced processes in neuromorphic devices and hardware to efficiently solve specific computing tasks. To achieve this the RCN is utilizing the extensive expertise within the network to investigate novel neuromorphic architectures, circuits and hardware that could lead to significant performance advantages when compared to other computing architectures that use central processing units (CPUs), graphical processing units (GPUs) or quantum processors. The RCN consists of three coordination sub-networks: (a) The NICE-RCN Co-design Framework is concentrating on the development of behavioral models and software. These tools will enable the simulation and evaluation of different architectures on benchmark tasks; (b) The NICE-RCN IC Design Framework will focus on designing neuromorphic Process Design Kits (PDKs) using open-source IC fabrication tools accessible through Efabless, as well as the Taiwan Semiconductor Manufacturing Corporation (TSMC) PDK tools provided by Muse Semiconductors; (c) The NICE-RCN IC Testing Framework will establish a testing infrastructure and create benchmarks for evaluating the performance of neuromorphic ICs.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.