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Convolution Primitives for Embedded Neural Networks on 32-bit Microcontrollers
The dataset used in this paper is a collection of state-of-the-art convolutional primitives for ARM Cortex-M microcontrollers. -
Quantum CNN Dataset
The dataset used in this paper is a dataset for training quantum convolutional neural networks. -
Quantum CNN
The dataset used in this paper is a quantum convolutional neural network (QCNN) dataset. -
Tensor decomposition to Compress Convolutional Layers in Deep Learning
Feature extraction for tensor data serves as an important step in many tasks such as anomaly detection, process monitoring, image classification, and quality control. -
Sorting of Smartphone Components for Recycling Through Convolutional Neural N...
A dataset with 1,127 images of pyrolyzed smartphone components, used to train and assess a VGG-16 image classification model for sorting waste electrical and electronic... -
SqueezeNet v1.1 and ZynqNet
The dataset used in this paper is the SqueezeNet v1.1 and ZynqNet CNNs, which are characterized by small model size and limited computational requirements. -
Residual and plain convolutional neural networks for 3d brain mri classification
Residual and plain convolutional neural networks for 3d brain mri classification -
FPGA deep learning acceleration based on convolutional neural network
This paper proposes a convolutional neural network hardware accelerator based on field programmable logic gate array (FPGA). -
Convnet-benchmarks
The dataset used in this paper is a benchmark suite for Convolutional Neural Networks. -
Caffe Framework with FPGA Support
The dataset used in this paper is a modified version of the Caffe CNN framework with support for FPGA implementations. -
Generic Framework for Convolution on Arbitrary Structures
The dataset used in the paper is a generic framework for convolution on arbitrary structures, which includes grid convolutions and graph convolutions. -
TDT4173 - Method Paper
A survey of the foundations, selected improvements, and some current applications of Deep Convolutional Neural Networks (CNNs). -
Hardware-Oriented Acceleration of Deep Convolutional Neural Networks
To address memory and computation resource limitations for hardware-oriented acceleration of deep convolutional neural networks (CNNs), we present a compu-tation flow, stacked... -
NESTA: A Specialized Neural Processing Engine for Efficient Convolutional Neu...
NESTA: a specialized neural processing engine designed for executing learning models in which filter-weights, input-data, and applied biases are expressed in fixed-point format. -
Technical Feasibility of Creating a Beach Grain Size Database with Citizen Sc...
The dataset used for training and testing the SediNet model for PSD determination.
