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With the fast increasingly use of image and video processing in many aspects, the requirements for high performance and high-quality systems lead to the use of reconfigurable computing to accelerate traditional image processing platforms. In this work, an efficient runtime adaptable floating-point Gaussian filtering core is proposed to achieve not only high performance and quality but also kernel...
"Girls who..." is an education system belonging to the French national program "Accompanying in Science and Technology in the Primary School" (ASTEP). "Girls who..." is a girl network that develops and maintains an facility called the factory, addressing a double goal: setting an example of science performed by women and foster science and technology in elementary schools...
Rebooting computing using in-memory architectures relies on the ability of emerging devices to execute a legacy software stack. In this paper, we present our approach of executing compute kernels written in a subset of the C programming language using flow-based computing on nanoscale memristor crossbars. Our approach also tests the correctness of the design using the parallel Xyces electronic simulation...
Since road markings are one of the main landmarks used for traffic guidance, perceiving them may be a crucial task for autonomous vehicles. In visual approaches, road marking detection consists in detecting pixels of an image that corresponds to a road marking. Recently, most approaches have aimed on detecting lane markings only, and few of them proposed methods to detect other types of road markings...
In this paper, we computationally predicted the interactions between HIV-1 and human proteins, based on the hypothesis that proteins with similar interface architecture share similar interaction partners. Evolution – aware protein structural alignment method UniAlign was used to calculate the similarity between two protein interface architectures. Using experimentally verified HIV-1, human protein-protein...
Estimating a depth map from multiple views of a scene is a fundamental task in computer vision. As soon as more than two viewpoints are available, one faces the very basic question how to measure similarity across >2 image patches. Surprisingly, no direct solution exists, instead it is common to fall back to more or less robust averaging of two-view similarities. Encouraged by the success of machine...
In this paper, we propose a 2-D grouping FIFO based FFT hardware architecture, supporting 36 different FFT sizes defined in 3GPP-LTE systems. Also, the important design foundation is to develop a hybrid-radix computing kernel engine, including 4 configuration types. In a design implementation via TSMC 90-nm CMOS technology, the reconfigurable FFT chip only has a core area occupation of 1.51 mm2, dissipating...
VGG 16 and Inception-v3 networks were trained using a texture dataset of muddied and clean cows. A new dataset with 600 images that is similar to the actual texture dataset was introduced and used to train the networks. The method used to train the networks was transfer learning. ImageNet weights were trained using the similar dataset, then the newly trained weights were trained again using the actual...
We propose associative domain adaptation, a novel technique for end-to-end domain adaptation with neural networks, the task of inferring class labels for an unlabeled target domain based on the statistical properties of a labeled source domain. Our training scheme follows the paradigm that in order to effectively derive class labels for the target domain, a network should produce statistically domain...
Stream processing applications have high-demanding performance requirements that are hard to tackle using traditional parallel models on modern many-core architectures, such as GPUs. On the other hand, recent dataflow computing models can naturally exploit parallelism for a wide class of applications. This work presents an extension to an existing dataflow library for Java. The library extension implements...
All semiconductor market domains are converging to concurrent platforms. This trend has certainly led real challenge to develop applications software that effectively uses these concurrent processors to achieve efficiency and performance goals. This paper argues that the Computer System related courses are natural places to introduce the parallelism, and the earlier to parallel computing concepts...
This paper deals with the problem of audio source separation. To handle the complex and ill-posed nature of the problems of audio source separation, the current state-of-the-art approaches employ deep neural networks to obtain instrumental spectra from a mixture. In this study, we propose a novel network architecture that extends the recently developed densely connected convolutional network (DenseNet),...
With the advent of new technologies in the field of medicine, there is rising awareness of biomechanisms, and we are better able to treat ailments than we could earlier. Deep learning has helped a lot in this endeavor. This paper deals with the application of deep learning in brain tumor segmentation. Brain tumors are difficult to segment automatically given the high variability in the shapes and...
Understanding the extent to which computational results can change across platforms, compilers, and compiler flags can go a long way toward supporting reproducible experiments. In this work, we offer the first automated testing aid called FLiT (Floating-point Litmus Tester) that can show how much these results can vary for any user-given collection of computational kernels. Our approach is to take...
Crowd counting on still images is very challenging due to heavy occlusions and scale variations. In this paper, we aim to develop a method that can accurately estimate the crowd count from a still image. Recently, convolutional neural networks have been shown effective in many computer vision tasks including crowd counting. To this end, we propose a fully convolutional network (FCN) architecture to...
Interconnected embedded machines in the Internet of Things (IoT) play a key role for collecting and managing data from the real environment. These machines adopt Machine-to-Machine (M2M) networks in order to exchange information. Securing this information is therefore essential. However, these machines have reduced capabilities giving the opportunity to be easily compromised. IoT trust agents in embedded...
During the last years, Convolutional Neural Networks (CNNs) have achieved state-of-the-art performance in image classification. Their architectures have largely drawn inspiration by models of the primate visual system. However, while recent research results of neuroscience prove the existence of non-linear operations in the response of complex visual cells, little effort has been devoted to extend...
In this paper, we present a simple and modularized neural network architecture, named interleaved group convolutional neural networks (IGCNets). The main point lies in a novel building block, a pair of two successive interleaved group convolutions: primary group convolution and secondary group convolution. The two group convolutions are complementary: (i) the convolution on each partition in primary...
In recent years, 3-dimension convolutional neural networks (3D CNNs) have been widely used for video analysis, 3-dimension geometric data and medical image diagnosis. While conventional CNNs are computationally intensive, 3D CNNs push the computational requirements into another level, since each computation depends on multiple image frames. This paper describes a novel hardware architecture for a...
Field-Programmable Gate Arrays (FPGAs) are gaining considerable momentum in mainstream high-performance systems in recent years due to their flexibility and low power consumption. Still, FPGAs remain largely unavailable to software programmers due to programming and debugging difficulties that are inherent to standard Hardware Description Languages. The performance that hardware-oblivious software...
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