Efficient Processing of Deep Neural Networks: A Tutorial and …
2017年3月27日 · Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity.
深度学习模型个人总结（DNN,CNN,RNN,LSTM,GCN,GAN）
2024年11月8日 · 神经网络是深度学习的基础，其中包括多种架构，如深度神经网络（dnn）、卷积神经网络（cnn）、循环神经网络（rnn）以及门控循环单元（lstm）。这些网络各有特点，适应不同的任务场景。
The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency
DNN、RNN、CNN.…..一文带你读懂这些绕晕人的名词 - 知乎
dnn：深度神经网络. 从结构上来说，dnn和传统意义上的nn（神经网络）并无太大区别，最大的不同是层数增多了，并解决了模型可训练的问题。 简言之，dnn比nn多了一些隐层，但这些隐层的作用是巨大的，带来的效果是非常显著和神奇的。
Efficient Processing of Deep Neural Networks | SpringerLink
The book includes background on DNN processing; a description and taxonomy of hardware architectural approaches for designing DNN accelerators; key metrics for evaluating and comparing different designs; features of DNN processing that are amenable to hardware/algorithm co-design to improve energy efficiency and throughput; and opportunities ...
ABSTRACT | Deep neural networks (DNNs) are currently widely used for many artificial intelligence (AI) applications including computer vision, speech recognition, and robotics. While DNNs deliver state-of-the-art accuracy on many AI tasks, it comes at the cost of high computational complexity.
Efficient Processing of Deep Neural Networks: A Tutorial and Survey
The reader will take away the following concepts from this article: understand the key design considerations for DNNs; be able to evaluate different DNN hardware implementations with benchmarks and comparison metrics; understand the tradeoffs between various hardware architectures and platforms; be able to evaluate the utility of various DNN ...
浅谈DNN（深度神经网络）算法原理 - CSDN博客
2025年2月18日 · 深度神经网络（Deep Neural Network, DNN）是一种基于人工神经网络的机器学习模型，它由多个隐藏层组成，能够自动从大量数据中学习复杂的模式和特征。 DNN 的基本结构包括输入层、多个隐藏层和输出层。
DNN：DL讨论与DNN经典论文汇总 - CSDN博客
2013年12月31日 · dnn更像是一个数据存储类型转换器，它把大量的训练样本以参数的形式存储在dnn里面；每一个参数模型对应了一个样本集合模型，在巨大的模型映射里面，如果建立的映射足够多，那么对模型的描述就足够稠密，对于其应用的领域便足够精确。
EFFECT-DNN: Energy-efficient Edge Framework for Real-time DNN …
In this paper, we address the trade-off between end-to-end latency of DNN inference and IoT devices’ energy consumption by proposing ‘EFFECT-DNN’, an energy efficient edge computing framework. The EFFECT-DNN framework aims to strike such balance by employing a collaborative DNN partitioning and task offloading strategy.