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的新型尖峰神经架构、训练算法和硬件计算设备，解决数据驱动应用广泛领域计算中当前的性能和能源效率危机。 来自生物大脑的灵感将被用来支持算法和硬件系统的开发，以缩小算力供需之间日益扩大的差距。该项目的成果将具有很强的跨学科性，预计将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。将通过各种外展计划促进本科生和代表性不足群体的研究参与。 该项目的成果将在广泛的研究和工业界传播，并纳入研究生课程。将寻求与工业界的研究合作，以指导这项工作解决现实世界的挑战，并为工业环境中的学生提供指导和培训。受大脑启发的计算模型和硬件计算系统有望提供后摩尔定律时代处理日益增多的数据所需的计算能力，而无需相应的高能源成本。该项目将专注于通过解决两个紧迫的相互依赖的研究障碍来提高现实学习任务中的尖峰神经模型的性能：缺乏计算能力强大的学习架构，以及缺乏可以有效训练复杂尖峰神经模型的实用算法。将探索神经科学和深度学习之间的协同作用，开发异构深度尖峰神经架构和学习算法，以解决相应的训练瓶颈。高效的尖峰神经处理器将在可重新配置的计算设备上进行演示。该奖项反映了 NSF 的法定使命，并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，被认为值得支持。