Convolutional Neural Networks (CNNs) are becoming a fundamental tool for machine learning. High performance and energy efficiency are of great importance for deployments of CNNs in many embedded applications. Energy consumption during CNN processing is dominated by memory access and since large networks do not fit in on-chip storage, they require expensive DRAM access. This paper introduces an universal Output Stream Manager (OSM) which can be used to compress and format data coming from a CNN accelerator and reduce external memory access. The OSM exploits the sparsity of data and implements two Zero-Run Length encoding algorithms and can be easily reconfigured to optimize usage for different CNN layers
Convolutional neural networks (CNNs) now also start to reach impressive performance on non-classific...
This thesis presents the results of an architectural study on the design of FPGA- based architecture...
Convolutional neural networks (CNN) provide state-of-the-art results in a wide variety of machine le...
Due to sparsity, a significant percentage of the operations carried out in Convolutional Neural Netw...
Convolutional neural networks (CNNs) require a huge amount of off-chip DRAM access, which accounts f...
After the tremendous success of convolutional neural networks in image classification, object detect...
Due to the huge success and rapid development of convolutional neural networks (CNNs), there is a gr...
Convolutional Neural Network (CNN) is a type of algorithm used to solve complex problems with a supe...
Deep convolutional neural networks (CNNs) generate intensive inter-layer data during inference, whic...
Convolutional neural networks (CNNs) have become the dominant neural network architecture for solvin...
Convolutional neural networks (CNNs) are one of the most successful machine-learning techniques for ...
In recent years, the convolutional neural network (CNN) has found wide acceptance in solving practic...
Convolutional Neural Networks (CNNs) allow fast and precise image recognition. Nowadays this capabil...
Convolutional Neural Networks (CNNs) have emerged as a fundamental technology for machine learning. ...
Convolutional neural networks (CNNs) have become the dominant neural network architecture for solvin...
Convolutional neural networks (CNNs) now also start to reach impressive performance on non-classific...
This thesis presents the results of an architectural study on the design of FPGA- based architecture...
Convolutional neural networks (CNN) provide state-of-the-art results in a wide variety of machine le...
Due to sparsity, a significant percentage of the operations carried out in Convolutional Neural Netw...
Convolutional neural networks (CNNs) require a huge amount of off-chip DRAM access, which accounts f...
After the tremendous success of convolutional neural networks in image classification, object detect...
Due to the huge success and rapid development of convolutional neural networks (CNNs), there is a gr...
Convolutional Neural Network (CNN) is a type of algorithm used to solve complex problems with a supe...
Deep convolutional neural networks (CNNs) generate intensive inter-layer data during inference, whic...
Convolutional neural networks (CNNs) have become the dominant neural network architecture for solvin...
Convolutional neural networks (CNNs) are one of the most successful machine-learning techniques for ...
In recent years, the convolutional neural network (CNN) has found wide acceptance in solving practic...
Convolutional Neural Networks (CNNs) allow fast and precise image recognition. Nowadays this capabil...
Convolutional Neural Networks (CNNs) have emerged as a fundamental technology for machine learning. ...
Convolutional neural networks (CNNs) have become the dominant neural network architecture for solvin...
Convolutional neural networks (CNNs) now also start to reach impressive performance on non-classific...
This thesis presents the results of an architectural study on the design of FPGA- based architecture...
Convolutional neural networks (CNN) provide state-of-the-art results in a wide variety of machine le...