SHF: Small: Cross-Platform Solutions for Pruning and Accelerating Neural Network Models

SHF：小型：用于修剪和加速神经网络模型的跨平台解决方案

• 批准号: 1744082
• 负责人: Hai Li
• 金额: $ 42.44万
• 依托单位: Duke University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2017
• 资助国家: 美国
• 起止时间: 2017-05-01 至 2019-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1744082&HistoricalAwards=false
• 关键词: SHF; Small; Cross; Platform; Solutions

Deep neural networks (DNNs) have achieved remarkable success in many applications because of their powerful capability for data processing. The objective of this project is to investigate a software-hardware co-design methodology for DNN acceleration that can be applied to both traditional von Neumann and emerging neuromorphic architectures. The project fits into the general area of "brain-inspired" energy efficient computing paradigms that has been of much recent interest. The investigators are also active in various outreach and educational activities that include curricular development, engagement of minority/underrepresented students in research. Undergraduate and graduate students involved in this research will also be trained for the next-generation computer engineering and semiconductor industry workforce.From a more technical standpoint, a novel neural network sparsification process is to be explored to preserve the state-of-the-art accuracy, while establishing hardware-friendly models of neural network computations. The result is expected to lead to a holistic methodology composed of neural network model sparsification, hardware acceleration, and an integrated software/hardware co-design. The project also benefits big data research and industry at large by inspiring an interactive design philosophy between the design of learning algorithms and the corresponding computational platforms for system performance and scalability enhancement.

深度神经网络（DNN）在许多应用程序中都取得了巨大的成功，因为它们具有强大的数据处理能力。该项目的目的是研究用于DNN加速的软件硬件共同设计方法，该方法可以应用于传统的von Neumann和新兴的神经形态体系结构。该项目符合“脑启发”能源有效计算范式的一般领域，这引起了人们的关注。调查人员还积极从事各种外展和教育活动，包括课程发展，少数民族/代表性不足的学生的参与。参与这项研究的本科生和研究生也将接受下一代计算机工程和半导体行业的劳动力的培训。从更具技术性的角度来看，将要探索一种新颖的神经网络稀疏过程，以保持最先进的准确性，同时建立神经网络计算的硬件友好型模型。 结果有望导致整体方法论由神经网络模型稀疏，硬件加速和集成的软件/硬件共同设计组成。 该项目还通过激发学习算法的设计与系统性能和可伸缩性增强的相应计算平台之间的交互式设计理念，从而使大数据研究和整个行业受益。