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Ponencia
On-The-Fly Deployment of Deep Neural Networks on Heterogeneous Hardware in a Low-Cost Smart Camera
(Association for Computing Machinery, 2018)
This demo showcases a low-cost smart camera where different hardware configurations can be selected to perform image recognition on deep neural networks. Both the hardware configuration and the network model can be changed ...
Artículo
Impact of thermal throttling on long-term visual inference in a cpu-based edge device
(Multidisciplinary Digital Publishing Institute (MDPI), 2020)
Many application scenarios of edge visual inference, e.g., robotics or environmental monitoring, eventually require long periods of continuous operation. In such periods, the processor temperature plays a critical role to ...
Ponencia
On the Correlation of CNN Performance and Hardware Metrics for Visual Inference on a Low-Cost CPU-based Platform
(Institute of Electrical and Electronics Engineers, 2019)
While providing the same functionality, the various Deep Learning software frameworks available these days do not provide similar performance when running the same network model on a particular hardware platform. On the ...
Artículo
Optimum Selection of DNN Model and Framework for Edge Inference
(IEEE, 2018)
This paper describes a methodology to select the optimum combination of deep neuralnetwork and software framework for visual inference on embedded systems. As a first step, benchmarkingis required. In particular, we have ...
Artículo
PreVIous: A Methodology for Prediction of Visual Inference Performance on IoT Devices
(Institute of Electrical and Electronics Engineers, 2020)
This article presents PreVIous, a methodology to predict the performance of convolutional neural networks (CNNs) in terms of throughput and energy consumption on vision-enabled devices for the Internet of Things. CNNs ...