Power-Aware FPGA Mapping of Convolutional Neural Networks

布景主题：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中表现出重大影响，并被各种行业采用。然而，当前的机器学习模型需要大量的计算，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。该项目将专注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耗和功耗，同时保持其性能并提供所需的处理能力。该研究将侧重于推导功耗模型，并将其集成到现有框架中，以优化机器学习算法到满足性能和能源/功率目标的嵌入式平台的映射。由于机器学习算法通常对近似值具有弹性，因此将研究针对机器学习工作负载量定制的新型有损技术，这些技术将提供必要的能量/功率增益，同时对系统的资源和性能影响最小。该项目的成果将是一套方法和工具，可以在嵌入式设备上部署基于机器学习的系统，因此，能够在减少的能量/功率占用中开发具有机器学习功能的物联网节点，从而延长系统的寿命。

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.