Deep Adder Networks on Edge Devices

该项目旨在通过开发先进的深度神经网络来为边缘设备提供智能，以解决深度学习的高资源需求与边缘性能普遍不足之间的冲突。乘法一直是深度学习中的主要操作类型，尽管加法已知要便宜得多。该项目预计将产生理论和算法，允许由几乎纯加法组成的深度神经网络在现实世界的计算机视觉任务中实现准确性，鲁棒性，校准和泛化。该项目的成功将使健康和网络安全领域的智能手机或机器人上基于深度学习的产品受益。

This project aims to empower edge devices with intelligence by developing advanced deep neural networks that address the conflict between the high resource requirements of deep learning and the generally inadequate performance of the edge. Multiplication has been the dominant type of operation in deep learning, though the addition is known to be much cheaper. This project expects to yield theories and algorithms that allow deep neural networks consisting of nearly pure additions to fulfil the requisites of accuracy, robustness, calibration and generalisation in real-world computer vision tasks. The success of this project will benefit deep learning-based products on smartphones or robots in health and cybersecurity.