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，训练和实现此类人工智能（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”致力于缩小人工智能计算效率的差距。该项目超越了当前神经形态计算架构对神经元和突触计算模型的关注，以检查可能有助于认知的生物大脑的其他计算单元。为此，这个 EAGER 项目将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，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，从而开辟新的方向。来自宾夕法尼亚州立大学施赖尔荣誉学院的教授将参与该项目，该项目的高度跨学科性质将极大地促进下一代学生的培训，他们将获得结合硬件知识的神经形态计算框架的设计知识。 PI 计划将该项目的结果整合到电气工程系 K-12 夏令营中。有关探索星形胶质细胞对自我修复的影响的先前文献主要局限于小规模网络，没有任何机器学习的视角。此外，主要从简单的软件模拟角度研究了星形胶质细胞功能的神经形态硬件实现也仅限于互补金属氧化物。半导体 (CMOS) 技术——由于 CMOS 晶体管和神经胶质细胞功能之间的功能不匹配，该技术的能源效率和面积效率都很高。为了弥补这一差距，拟议的研究议程探索了一种硬件-软件协同设计方法，将神经胶质细胞功能纳入神经形态中。 EAGER 计划重点关注以下研究重点：（i）利用星形胶质细胞计算模型来评估对自我修复至关重要的神经胶质功能的各个方面。在神经形态机器学习平台的背景下，（ii）探索自旋电子器件和电路原语，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，其中底层设备通过其内在物理特性模拟星形胶质细胞的功能，以及（iii）结合上述自上而下和自下而上的观点，在硬件实际故障（例如电阻漂移、寄生效应、设备到设备变化等）的背景下利用星形胶质细胞的自我修复拟议的研究议程将为开发能够自主修复非理想硬件操作的新一代神经形态平台提供概念验证结果。该奖项反映了有效的法定使命，并已获得通过使用基金会的智力优点和更广泛的影响审查标准进行评估，认为值得支持。

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