In this study, we investigate the efficacy of Convolutional Neural networks (CNN), Recurrent Neural networks (RNN), Long Short-Term Memory networks (LSTM), and Bidirectional LSTMs (BiLSTM) for emotion classification t...
详细信息
Spectrum sensing (SS) uses cognitive radio technology at base transceiver stations in order to discover the licensed spectra used by Primary Users (PUs). This enables the SUs to make wireless network transmissions wit...
详细信息
Network-on-Chips (NoCs) are the standard communication fabrics for interconnecting cores and uncores in multi/many-core systems. Deep learning applications can be running efficiently on NoCs because of the scalability...
详细信息
A wounded part's defence mechanism, pain, is meant to keep it from getting worse. Worldwide, people are impacted by Low Back Pain (LBP), a prevalent, debilitating, and burdensome illness. Trauma, inappropriate use...
详细信息
This paper addresses uneven terrain coverage and attempts to develop a maximally compact coverage with a uniform random distribution of n drones. We focus on an efficient drone placement approach so that, in a self-or...
详细信息
The increased demand for big data processing has led to the development of distributed computing and data storage technologies. However, building an efficient distributed system is a rather difficult task and has a fe...
详细信息
It is well-established that terrestrial communication systems may fail during emergencies such as earthquakes, tsunamis, and floods. Fortunately, with the rapid advancement of unmanned aerial vehicle (UAV) network tec...
详细信息
Software-defined network (SDN), a centralized network control architecture, distributed SDN (DSDN)operating systems, and applications are being created to meet the demands of fault tolerance and scalability. However, ...
详细信息
We present NADA, a Network Attached Deep learning Accelerator. It provides a flexible hardware/software framework for training deep neural networks on ethernet-based FPGA clusters. The NADA hardware framework instanti...
详细信息
ISBN:
(纸本)9783031661457;9783031661464
We present NADA, a Network Attached Deep learning Accelerator. It provides a flexible hardware/software framework for training deep neural networks on ethernet-based FPGA clusters. The NADA hardware framework instantiates a dedicated entity for each layer in a model. Features and gradients flow through these entities in a tightly pipelined manner. From a compact description of a model and target cluster, the NADA software framework generates specific configuration bitstreams for each particular FPGA in the cluster. We demonstrate the scalability and flexibility of our approach by mapping an example CNN onto a cluster consisting of three up to nine Intel Arria 10 FPGAs. To verify NADAs effectiveness for commonly used networks, we train MobileNetV2 on a six-node cluster. We address the inherent incompatibility of the tightly pipelined layer parallel approach with batch normalization by using online normalization instead.
The proceedings contain 9 papers. The special focus in this conference is on distributed Computing and Artificial Intelligence. The topics include: A Deep Learning-Based OCR System Implementation for Traceability...
ISBN:
(纸本)9783031739095
The proceedings contain 9 papers. The special focus in this conference is on distributed Computing and Artificial Intelligence. The topics include: A Deep Learning-Based OCR System Implementation for Traceability Ensurement in a Metal Manufacturing Workshop;dimensional Reduction Techniques for the Characterization of Behavioral Patterns in Dairy Cows;geothermal Heat Exchanger’s Temperature Input Sensor Prediction Based on Deep Learning Modelling Technique;a Hybrid Intelligence Model Forecasts the Temperature of a Battery Used in Electric Vehicles;a Comparative Analysis of Algorithms and Metrics to Perform Clustering;wind Speed Virtual Sensor for Small Wind Turbine;reconstructing Turbulence-Distorted Wavefronts Through Laser-Beam Profiles.
暂无评论