Efficient weights quantization of convolutional neural networks using kernel density estimation based non-uniform quantizer
Convolutional neural networks (CNN) have achieved excellent results in the field of image
recognition that classifies objects in images. A typical CNN consists of a deep architecture …
recognition that classifies objects in images. A typical CNN consists of a deep architecture …
Methodologies of compressing a stable performance convolutional neural networks in image classification
Deep learning has made a real revolution in the embedded computing environment.
Convolutional neural network (CNN) revealed itself as a reliable fit to many emerging …
Convolutional neural network (CNN) revealed itself as a reliable fit to many emerging …
Retrain or not retrain?-efficient pruning methods of deep cnn networks
M Pietron, M Wielgosz - … Conference, Amsterdam, The Netherlands, June 3 …, 2020 - Springer
Nowadays, convolutional neural networks (CNN) play a major role in image processing
tasks like image classification, object detection, semantic segmentation. Very often CNN …
tasks like image classification, object detection, semantic segmentation. Very often CNN …
Effective post-training quantization of neural networks for inference on low power neural accelerator
A Demidovskij, E Smirnov - 2020 International Joint Conference …, 2020 - ieeexplore.ieee.org
Neural network deployment to the target environment is considered a challenging task
especially because of heavy burden of hardware requirements that DNN models lay on …
especially because of heavy burden of hardware requirements that DNN models lay on …
[PDF][PDF] Compression of convolutional neural network for natural language processing
Abstract Convolutional Neural Networks (CNNs) were created for image classification tasks.
Quickly, they were applied to other domains, including Natural Language Processing (NLP) …
Quickly, they were applied to other domains, including Natural Language Processing (NLP) …
Deep Convolutional Tables: Deep Learning Without Convolutions
S Dekel, Y Keller, A Bar-Hillel - IEEE Transactions on Neural …, 2023 - ieeexplore.ieee.org
We propose a novel formulation of deep networks that do not use dot-product neurons and
rely on a hierarchy of voting tables instead, denoted as convolutional tables (CTs), to enable …
rely on a hierarchy of voting tables instead, denoted as convolutional tables (CTs), to enable …
[PDF][PDF] Apache Hadoop performance evaluation with resources monitoring tools, and parameters optimization: IOT emerging demand
Recently, IoT has revealed a key value in the smart cities. Our living comfortability level has
been improved. Such technology requires extensive data processing especially when it is a …
been improved. Such technology requires extensive data processing especially when it is a …
Software-driven design optimization for mapping between floating-point and fixed-point multiply accumulators
An example multiply accumulate (MACC) circuit includes a multiply-accumulator having an
accumulator output register, a scaler, coupled to the multiply accumulator, and a control …
accumulator output register, a scaler, coupled to the multiply accumulator, and a control …
[PDF][PDF] Detecting Turistic Places with Convolutional Neural Networks.
F Torrico-Pacherre, I Maguina-Mendoza, W Ugarte - ICEIS (1), 2022 - scitepress.org
A mobile application was developed for the recognition of places from a photo using the
technique “content based photo geolocation as spatial database queries”. For this purpose …
technique “content based photo geolocation as spatial database queries”. For this purpose …
Face recognition and machine learning at the edge
The number of IoT is expected to reach 20 billion by year 2020. This is due to data that log in
the sensors or cameras are all send to the cloud for further processing. Cloud computing is …
the sensors or cameras are all send to the cloud for further processing. Cloud computing is …