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Enabling all in-edge deep learning: A literature review
In recent years, deep learning (DL) models have demonstrated remarkable achievements
on non-trivial tasks such as speech recognition, image processing, and natural language …
on non-trivial tasks such as speech recognition, image processing, and natural language …
Forms: Fine-grained polarized reram-based in-situ computation for mixed-signal dnn accelerator
Recent work demonstrated the promise of using resistive random access memory (ReRAM)
as an emerging technology to perform inherently parallel analog domain in-situ matrix …
as an emerging technology to perform inherently parallel analog domain in-situ matrix …
Compiler-aware neural architecture search for on-mobile real-time super-resolution
Deep learning-based super-resolution (SR) has gained tremendous popularity in recent
years because of its high image quality performance and wide application scenarios …
years because of its high image quality performance and wide application scenarios …
Pruning via iterative ranking of sensitivity statistics
S Verdenius, M Stol, P Forré - ar** of input-output channels
J Zhu, J Pei - Neurocomputing, 2022 - Elsevier
As the smallest structural unit of feature map**, the convolution kernel in a deep
convolution neural networks (DCNN) convolutional layer is responsible for the input channel …
convolution neural networks (DCNN) convolutional layer is responsible for the input channel …
Target attention deep neural network for infrared image enhancement
The inherent high background radiation and low contrast of infrared images severely cripple
the precision of target detection and recognition. However, existing infrared image …
the precision of target detection and recognition. However, existing infrared image …
Enabling retrain-free deep neural network pruning using surrogate lagrangian relaxation
D Gurevin - 2022 - search.proquest.com
Network pruning is a widely used technique to reduce computation cost and model size for
deep neural networks. However, the typical three-stage pipeline, ie, training, pruning and …
deep neural networks. However, the typical three-stage pipeline, ie, training, pruning and …
Design automation for fast, lightweight, and effective deep learning models: A survey
Deep learning technologies have demonstrated remarkable effectiveness in a wide range of
tasks, and deep learning holds the potential to advance a multitude of applications …
tasks, and deep learning holds the potential to advance a multitude of applications …
All-in-one: A highly representative dnn pruning framework for edge devices with dynamic power management
During the deployment of deep neural networks (DNNs) on edge devices, many research
efforts are devoted to the limited hardware resource. However, little attention is paid to the …
efforts are devoted to the limited hardware resource. However, little attention is paid to the …
Moc: Multi-objective mobile cpu-gpu co-optimization for power-efficient dnn inference
With the emergence of DNN applications on mobile devices, plenty of attention has been
attracted to their optimization. However, the impact of DNN inference tasks on device power …
attracted to their optimization. However, the impact of DNN inference tasks on device power …