Recent advances in convolutional neural network acceleration
In recent years, convolutional neural networks (CNNs) have shown great performance in
various fields such as image classification, pattern recognition, and multi-media …
various fields such as image classification, pattern recognition, and multi-media …
Accelerating neural network inference on FPGA-based platforms—A survey
R Wu, X Guo, J Du, J Li - Electronics, 2021 - mdpi.com
The breakthrough of deep learning has started a technological revolution in various areas
such as object identification, image/video recognition and semantic segmentation. Neural …
such as object identification, image/video recognition and semantic segmentation. Neural …
Data-driven mineral prospectivity mapping by joint application of unsupervised convolutional auto-encoder network and supervised convolutional neural network
The excellent performance of convolutional neural network (CNN) and its variants in image
classification makes it a potential perfect candidate for dealing with multi-geoinformation …
classification makes it a potential perfect candidate for dealing with multi-geoinformation …
[PDF][PDF] Ease: Energy optimization through adaptation–a review of runtime energy-aware approximate deep learning algorithms
EASE: Energy Optimization through Adaptation – A Review of Runtime Energy-Aware
Approximate Deep Learning Algorithms Page 1 P osted on 6 F eb 2024 — CC-BY 4.0 — h …
Approximate Deep Learning Algorithms Page 1 P osted on 6 F eb 2024 — CC-BY 4.0 — h …
Detection and comparison of reversible shape transformations in responsive polymers using deep learning and knowledge transfer by identifying stimulus-triggering …
C Abhishek, N Raghukiran - Engineering Applications of Artificial …, 2024 - Elsevier
Responsive polymers can alter their properties or shape according to stimuli such as stress,
light, among others. The stimuli may be strategically programmed at assigned triggering …
light, among others. The stimuli may be strategically programmed at assigned triggering …
Enhancing subsurface contamination assessment via ensemble prediction of ground electrical property: A Colorado AMD-impacted wetland case study
Acid mine drainage (AMD) is recognized as a major environmental challenge in the Western
United States, particularly in Colorado, leading to extreme subsurface contamination issue …
United States, particularly in Colorado, leading to extreme subsurface contamination issue …
Deep neural networks on chip-a survey
Currently, deep neural networks (DNNs) are widely used for various applications and have
achieved state-of-the-art performances. A survey about the prior researches addressing …
achieved state-of-the-art performances. A survey about the prior researches addressing …
A stochastic logic-based fuzzy logic controller: First experimental results of a novel architecture
In stochastic computing (SC) systems numbers are represented with mean values of random
binary sequences. This paper introduces a novel fuzzy inference architecture, in which the …
binary sequences. This paper introduces a novel fuzzy inference architecture, in which the …
Approximate logic synthesis: A reinforcement learning-based technology mapping approach
G Pasandi, S Nazarian… - … Symposium on Quality …, 2019 - ieeexplore.ieee.org
Approximate Logic Synthesis (ALS) is the process of synthesizing and mapping a given
Boolean network to a library of logic cells so that the magnitude/rate of error between …
Boolean network to a library of logic cells so that the magnitude/rate of error between …
Accelerating deep convolutional neural network base on stochastic computing
Abstract Deep Convolutional Neural Networks (DCNNs) are highly computational, and low
budget platforms face many restrictions due to their implementation. Recently, Stochastic …
budget platforms face many restrictions due to their implementation. Recently, Stochastic …