4.6 Article

MixedNet: Network Design Strategies for Cost-Effective Quantized CNNs

相关参考文献

注意:仅列出部分参考文献,下载原文获取全部文献信息。
Article Computer Science, Hardware & Architecture

AI Driven Heterogeneous MEC System with UAV Assistance for Dynamic Environment: Challenges and Solutions

Feibo Jiang et al.

Summary: The article discusses the concept of Mobile Edge Computing (MEC) and proposes a Heterogeneous MEC (H-MEC) architecture that includes both fixed units and moving nodes. It also addresses the key challenges in H-MEC and potential AI-based solutions, introducing an AI-based joint Resource schEduling (ARE) framework with two different mechanisms. Simulation results are provided to verify the efficiency of the proposed framework.

IEEE NETWORK (2021)

Article Computer Science, Artificial Intelligence

Squeeze-and-Excitation Networks

Jie Hu et al.

IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE (2020)

Article Computer Science, Information Systems

Deep-Learning-Based Joint Resource Scheduling Algorithms for Hybrid MEC Networks

Feibo Jiang et al.

IEEE INTERNET OF THINGS JOURNAL (2020)

Article Engineering, Electrical & Electronic

Compact Mixed-Signal Convolutional Neural Network Using a Single Modular Neuron

Dong-Jin Chang et al.

IEEE TRANSACTIONS ON CIRCUITS AND SYSTEMS I-REGULAR PAPERS (2020)

Article Engineering, Electrical & Electronic

An Always-On 3.8 μJ/86% CIFAR-10 Mixed-Signal Binary CNN Processor With All Memory on Chip in 28-nm CMOS

Daniel Bankman et al.

IEEE JOURNAL OF SOLID-STATE CIRCUITS (2019)

Article Engineering, Electrical & Electronic

In-Memory Computation of a Machine-Learning Classifier in a Standard 6T SRAM Array

Jintao Zhang et al.

IEEE JOURNAL OF SOLID-STATE CIRCUITS (2017)

Proceedings Paper Engineering, Electrical & Electronic

DNPU: An 8.1TOPS/W Reconfigurable CNN-RNN Processor for General-Purpose Deep Neural Networks

Dongjoo Shin et al.

2017 IEEE INTERNATIONAL SOLID-STATE CIRCUITS CONFERENCE (ISSCC) (2017)

Review Multidisciplinary Sciences

Deep learning

Yann LeCun et al.

NATURE (2015)

Article Engineering, Electrical & Electronic

90% write power-saving SRAM using sense-amplifying memory cell

K Kanda et al.

IEEE JOURNAL OF SOLID-STATE CIRCUITS (2004)