Mapping deep learning algorithms on systems-on chip

在片上系统上映射深度学习算法

• 批准号: 531142-2018
• 负责人: Nicolescu, Gabriela
• 金额: $ 5.7万
• 依托单位: École Polytechnique de Montréal
• 依托单位国家: 加拿大
• 项目类别: Collaborative Research and Development Grants
• 财政年份: 2020
• 资助国家: 加拿大
• 起止时间: 2020-01-01 至 2021-12-31
• 项目状态: 已结题
• 来源: https://www.nserc-crsng.gc.ca/ase-oro/Details-Detailles_eng.asp?id=697731
• 关键词: Mapping; deep; learning; algorithms; systems

This project aims to define an innovative approach for mapping deep learning neural networks of embedded autonomous systems communicating in the Internet of Things (IoT). Autonomous systems must be able to perform a series of operations determined by the outcome of the previous operation, or by reference to external circumstances monitored and measured by the system itself. Today's autonomous systems work well within defined and controlled situations, and there is huge potential for further development in this area. There are, however, still concerns and constraints. This is mainly because in the future IoT, autonomous systems will exchange a continuous flow of massive data, and each system must be able to handle the data and activate appropriate responses quickly, safely, and securely, while consuming little power. This requires a variety of complex decision-making, learning and data processing by these systems. This proposed research project will develop solutions to key challenges in the design, implementation, and deployment of autonomous systems in the IoT, namely: - New methods of adapting deep learning, the key algorithms in the autonomous systems of the future, to the tight performance and power constraints specific to edge computing and autonomous systems; - New tools for automatic, optimized, deployment of these extremely complex learning algorithms on embedded platforms. We are particularly concerned with a set of communicating algorithms, specific to learning, computer vision and voice recognition, mapped on dedicated platforms integrating multi-core architectures and neural network accelerators, such as the Synopsys EV6x.

该项目旨在定义一种创新方法，用于映射在物联网（IoT）中通信的嵌入式自主系统的深度学习神经网络。自主系统必须能够执行一系列操作，这些操作是由前一个操作的结果决定的，或者是由系统自身监控和测量的外部环境决定的。今天的自主系统在定义和控制的情况下工作得很好，并且在这一领域有进一步发展的巨大潜力。然而，仍然存在一些担忧和制约因素。这主要是因为在未来的物联网中，自治系统将交换连续的大量数据流，每个系统必须能够快速、安全、安全地处理数据并激活适当的响应，同时消耗很少的功率。这需要这些系统进行各种复杂的决策、学习和数据处理。