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）因其强大的数据处理能力而在许多应用中取得了显着的成功。该项目的目标是研究一种用于 DNN 加速的软件-硬件协同设计方法，该方法可应用于传统的冯诺依曼和新兴的神经形态架构。该项目符合最近引起人们广泛关注的“受大脑启发”的节能计算范式的一般领域。研究人员还积极参与各种外展和教育活动，包括课程开发、少数族裔/代表性不足的学生参与研究。参与这项研究的本科生和研究生还将接受下一代计算机工程和半导体行业劳动力的培训。从更技术的角度来看，将探索一种新颖的神经网络稀疏化过程，以保留最先进的技术准确性，同时建立神经网络计算的硬件友好模型。 预计结果将形成一种由神经网络模型稀疏化、硬件加速和集成软件/硬件协同设计组成的整体方法。 该项目还通过激发学习算法设计与相应计算平台之间的交互设计理念来增强系统性能和可扩展性，从而使大数据研究和整个行业受益。