FET: Small: Heterogeneous Learning Architectures and Training Algorithms for Hardware Accelerated Deep Spiking Neural Computation

FET：小型：硬件加速深度尖峰神经计算的异构学习架构和训练算法

• 批准号: 1911067
• 负责人: Peng Li
• 金额: $ 49.93万
• 依托单位: Texas A&M Engineering Experiment Station
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-10-01 至 2019-11-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1911067&HistoricalAwards=false
• 关键词: FET; Small; Heterogeneous; Learning; Architectures

This project aims to address the present performance and energy efficiency crisis in computing across broad areas of data-driven applications by developing energy-efficient new spiking neural architectures, training algorithms, and hardware computing devices. Inspirations from biological brains will be taken to support the development of algorithms and hardware systems to close the widening gap between the supply and demand of computing power. The outcomes from this project will be strongly interdisciplinary and are expected to stimulate technical advancements in machine learning and bridge between neural networks, neuroscience, and hardware engineering. The research will provide rich training and educational opportunities to students. Research participation from undergraduate students and underrepresented groups will be promoted through various outreach programs. The results of this project will be disseminated in broad research and industrial communities and integrated into the graduate-level curriculum. Research collaboration with industry will be sought to guide this work toward addressing real-world challenges and provide mentoring and training of students in the industrial setting. Brain-inspired models of computation and hardware computing systems hold the promise of delivering the amount of computing power required in processing increasingly large volumes of data in the post Moore's Law era, without a correspondingly high energy cost. This project will focus on improving the performances of spiking neural models for real-life learning tasks by addressing two pressing inter-dependent research roadblocks: lack of computationally powerful learning architectures, and lack of practical algorithms that can effectively train complex spiking neural models. Synergies between neuroscience and deep learning will be explored to develop heterogeneous deep spiking neural architectures and learning algorithms to address the corresponding training bottlenecks. Efficient spiking neural processors will be demonstrated on reconfigurable computing devices.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.

该项目旨在通过开发节能新的尖峰神经体系结构，培训算法和硬件计算设备来解决当前数据驱动应用程序跨数据驱动应用程序的计算危机。 将采取来自生物大脑的灵感来支持算法和硬件系统的开发，以缩小计算能力供求之间的扩大差距。该项目的结果将是强烈的跨学科，并有望刺激机器学习的技术进步以及神经网络，神经科学和硬件工程之间的桥梁。该研究将为学生提供丰富的培训和教育机会。本科生和代表性不足的小组的研究参与将通过各种外展计划促进。 该项目的结果将在广泛的研究和工业社区中传播，并融入研究生级课程中。将寻求与行业的研究合作，以指导这项工作，以应对现实世界中的挑战，并在工业环境中为学生提供指导和培训。脑启发的计算和硬件计算系统的模型具有提供在摩尔法律时代处理日益大量数据所需的计算能力量，而没有相应的高能量成本。该项目将着重于通过解决两个紧迫的相互依存的研究障碍来改善现实生活中的神经模型的表现：缺乏计算功能强大的学习体系结构，以及缺乏可以有效地训练复杂尖峰神经模型的实用算法。神经科学和深度学习之间的协同作用将探索，以开发异质的深度尖峰神经体系结构和学习算法，以解决相应的培训瓶颈。可在可重构计算设备上证明有效的尖峰神经处理器。该奖项反映了NSF的法定任务，并使用基金会的知识分子优点和更广泛的影响审查标准，被认为值得通过评估来获得支持。