A quantization framework for neural network adaption at the edge

Learn to Learn on Chip: Hardware-aware Meta-learning for Quantized Few-shot Learning at the Edge

NS Murthy, P Vrancx, N Laubeuf… - 2022 IEEE/ACM 7th …, 2022 - ieeexplore.ieee.org
Recent years have seen a growing trend of deploying deep neural network-based
applications on edge devices. Many of these applications, such as biometric identification …
被引用次数：2 相关文章 所有 3 个版本

Computational and Storage Efficient Quadratic Neurons for Deep Neural Networks

C Chen, GL Zhang, X Yin, C Zhuo… - … , Automation & Test …, 2024 - ieeexplore.ieee.org
Deep neural networks (DNNs) have been widely deployed across diverse domains such as
computer vision and natural language processing. However, the impressive …
相关文章 所有 3 个版本