On Building Efficient and Robust Neural Network Designs

2022 56th Asilomar Conference on Signals, Systems, and Computers(2022)

引用 0|浏览22
暂无评分
摘要
Neural network models have demonstrated outstanding performance in a variety of applications, from image classification to natural language processing. However, deploying the models to hardware raises efficiency and reliability issues. From the efficiency perspective, the storage, computation, and communication cost of neural network processing is considerably large because the neural network models have a large number of parameters and operations. From the standpoint of robustness, the perturbation in hardware is unavoidable and thus the performance of neural networks can be degraded. As a result, this paper investigates effective learning and optimization approaches as well as advanced hardware designs in order to build efficient and robust neural network designs.
更多
查看译文
关键词
efficiency,robustness,neural network,hardware-software co-design
AI 理解论文
溯源树
样例
生成溯源树,研究论文发展脉络
Chat Paper
正在生成论文摘要