EAGER: Exploring the Self-Repair Role of Astrocytes in Neuromorphic Computing

EAGER：探索星形胶质细胞在神经形态计算中的自我修复作用

• 批准号: 2031632
• 负责人: Abhronil Sengupta
• 金额: $ 30万
• 依托单位: Pennsylvania State Univ University Park
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-09-01 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2031632&HistoricalAwards=false
• 关键词: EAGER; Exploring; Self; Repair; Role

With the unprecedented success of deep learning in pattern recognition tasks, the demands for computational expenses required to train and implement such Artificial Intelligence (AI) systems have also grown beyond current capabilities. “Neuromorphic Computing” strives to reduce the gap in computational efficiency of AI platforms by exploring bio-plausibility in the underlying computational primitives and hardware substrate. This project goes beyond the focus of current neuromorphic computing architectures on computational models for neuron and synapse to examine other computational units of the biological brain that might contribute to cognition and especially self-repair. To this end, this EAGER project will forge new directions by drawing inspiration and insights from computational neuroscience regarding functionalities of glial cells and explores their role in the fault-tolerant capacity of emerging hardware enabled neuromorphic computing platforms. Graduate students and undergraduates from Penn State's Schreyer Honors College will be involved in the project. The highly interdisciplinary nature of the project will contribute significantly to the training of next generation students who will gain knowledge in the design of neuromorphic computing frameworks combining knowledge from hardware, neuroscience and machine learning. The PI plans to integrate the results from this project into the Electrical Engineering departmental K-12 summer camp.Prior literature on exploring impact of astrocytes on self-repair has been primarily confined to small scale networks without any machine learning perspective. Further, self-repair has been studied primarily from a simplistic software simulation standpoint with stuck-at-zero faults. Neuromorphic hardware implementations for astrocyte functionalities have been also limited to Complementary Metal Oxide Semiconductor (CMOS) technology – which is highly energy and area inefficient due to the functional mismatch between CMOS transistors and glial functionality. To bridge this gap, the proposed research agenda explores a hardware-software co-design approach to incorporate glial cell functionality in neuromorphic platforms through the usage of spintronic technologies. The EAGER program focuses on the following research thrusts: (i) Exploiting astrocyte computational models to evaluate the aspects of glial functionality crucial for self-repair in the context of neuromorphic machine learning platforms, (ii) Exploring spintronic device and circuit primitives to design a coupled neuron-synapse-astrocyte network capable of self-repair where the underlying device mimics the astrocyte functionality through their intrinsic physics, and (iii) Combination of the above top-down and bottom-up perspectives to leverage astrocyte self-repair in the context of hardware realistic faults like resistance drift, parasitic effects, device to device variations, among others in neuromorphic AI systems. The proposed research agenda, would provide proof-of-concept results toward the development of a new generation of efficient neuromorphic platforms that are able to autonomously self-repair non-ideal hardware operation.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.

随着深度学习在模式识别任务中取得前所未有的成功，训练和实施此类人工智能（AI）系统所需的计算费用需求也超出了当前的能力。“神经形态计算”致力于通过探索底层计算原语和硬件基底中的生物相容性来缩小AI平台计算效率的差距。这个项目超越了 当前神经形态计算架构的重点是神经元和突触的计算模型，以检查可能有助于认知，特别是自我修复的生物大脑的其他计算单元。 为此，EAGER项目将通过从计算神经科学中汲取有关神经胶质细胞功能的灵感和见解来开拓新的方向，并探索它们在新兴硬件支持的神经形态计算平台的容错能力中的作用。来自宾夕法尼亚州立大学Schreyer荣誉学院的研究生和本科生将参与该项目。该项目的高度跨学科性质将大大有助于培养下一代学生，他们将获得结合硬件，神经科学和机器学习知识的神经形态计算框架设计方面的知识。PI计划将该项目的结果整合到电气工程系的K-12夏令营中。之前关于探索星形胶质细胞对自我修复的影响的文献主要局限于小规模网络，没有任何机器学习的观点。此外，自我修复主要是从一个简单的软件模拟的角度与固定在零故障进行了研究。用于星形胶质细胞功能的神经形态硬件实现也受限于互补金属氧化物半导体（CMOS）技术，由于CMOS晶体管和神经胶质功能之间的功能失配，CMOS技术是高度能量和面积低效的。为了弥合这一差距，拟议的研究议程探讨了硬件-软件协同设计方法，通过使用自旋电子技术将神经胶质细胞功能纳入神经形态平台。EAGER计划侧重于以下研究方向：（i）利用星形胶质细胞计算模型来评估神经形态机器学习平台背景下对自我修复至关重要的神经胶质功能方面，（ii）探索自旋电子器件和电路基元以设计能够自我修复的耦合神经元-突触-星形胶质细胞网络，其中底层器件通过其内在物理学模拟星形胶质细胞功能，以及（iii）结合上述自上而下和自下而上的观点，以在神经形态AI系统中的硬件现实故障（如电阻漂移、寄生效应、设备到设备的变化等）的背景下利用星形胶质细胞自我修复。拟议的研究议程，将提供概念验证的结果，朝着新一代的高效神经形态平台，能够自主自我修复非理想的硬件操作的发展。这个奖项反映了NSF的法定使命，并已被认为是值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估的支持。

项目成果

Astromorphic Self-Repair of Neuromorphic Hardware Systems

神经形态硬件系统的天体自我修复

• DOI: 10.1609/aaai.v37i6.25947
• 发表时间: 2023
• 期刊: Proceedings of the AAAI Conference on Artificial Intelligence
• 作者: Han, Zhuangyu;Islam, A N;Sengupta, Abhronil
• 通讯作者: Sengupta, Abhronil