Power-Aware FPGA Mapping of Convolutional Neural Networks

主题：数字经济领域：人工智能技术近年来，机器学习和人工智能技术在许多应用程序中显示出重大影响，并已被多个行业采用。然而，目前的机器学习模型需要大量的计算，这使得它们在计算和能量受限的嵌入式设备上的部署成为一个具有挑战性的问题。该项目将侧重于研究和开发方法和工具，以减少在嵌入式设备上部署机器学习算法的能源和电力消耗，同时保持其性能并提供所需的处理能力。这项研究将专注于推导功耗模型，并将其集成到现有框架中，以允许优化机器学习算法到嵌入式平台的映射，以满足性能和能源/功率目标。由于机器学习算法通常对近似具有弹性，因此将研究为机器学习工作负载量身定做的新型有损技术，这些技术将在对系统资源和性能影响最小的情况下提供必要的能量/功率增益。该项目的结果将是一套方法和工具，将允许在嵌入式设备上部署基于机器学习的系统，从而使具有机器学习能力的物联网节点的开发能够以减少的能量/功率足迹实现，从而延长系统的使用寿命。

Theme: Digital EconomyArea: Artificial intelligence technologiesIn recent years, Machine Learning and Artificial Intelligence techniques have demonstrated significant impact in a number of applications and have adopted by a variety of industries. Nevertheless, the current machine learning models require a large number of computations, making their deployment on embedded devices that are compute and energy constrained a challenging problem. The project will focus on the research and development of methodologies and tools that would enable the reduction of energy and power consumption of the deployment of machine learning algorithms on an embedded device, maintaining at the same time their performance and delivering the required processing capability. The research will focus on the derivation of models for power consumption and their integration in existing frameworks that allow to optimise the mapping of a Machine Learning algorithm into an embedded platform meeting performance and energy/power targets. As machine learning algorithms are in general resilient to approximations, novel lossy techniques will be investigated tailored for machine learning workloads that would deliver the necessary energy/power gains, with minimum impact on the resources and performance of the system.The outcome of the project will be a set of methodologies and tools that would allow the deployment of machine learning based systems on embedded devices, enabling as such the development of IoT nodes with machine learning capabilities in a reduced energy/power footprint, leading to the extended life of the system.