[PDF] Top 20 Efficient FPGA-Based Inference Architectures for Deep Learning Networks

... Neural Networks (CNNs) and Deep Neural Networks (DNNs) have gained significant popularity in several classification and regres- sion ...CNN architectures pose particular challenges for ... Voir le document complet

... audio-visual architectures have been recently ...overall inference problem is ...of inference networks variational autoencoder ...encoder networks input, respectively, audio and visual ... Voir le document complet

... of Deep Learning (DL) algorithms in computer vi- sion tasks have created an on-going demand of dedicated hard- ware architectures that could keep up with the their required com- putation and memory ... Voir le document complet

... neural networks (CNNs) has been developed for a wide range of applications such as image recognition, nature language processing, ...of deep CNNs in home and mobile devices remains challenging due to ... Voir le document complet

... Machine learning, Heterogeneous architecture, FPGA, Embedded systems ...paradigm for Internet-of-Things (IoT) systems, where computations are distributed across a broad range of compact devices, so ... Voir le document complet

... xc7z045ffg900 FPGA device, and perform two types of power estimation: the first corresponds to the classic power estimation that is achieved in the last steps of an FPGA design flow (after place and route ... Voir le document complet

... neural networks (FFNNs) such as CNNs and real-valued RNNs have shown to excel in a wide variety of applications and learning ...task. For instance, recurrent neural networks are characterized ... Voir le document complet

... hand, for the residual based approaches it is easier to learn to predict zero residuals in the case of correct initial labels, but it is more difficult for them to refine “hard” mistakes that deviate ... Voir le document complet

... features. Based on this result, numerous methods have been proposed in order to remove network weights (weight sparsification) either on pre-trained models or during the training ... Voir le document complet

... heterogeneous FPGA-GPU ...CNN architectures on an FPGA-GPU embedded heterogeneous ...an FPGA exploiting Direct Hardware Mapping (DHM) outperforms a GPU im- plementation on a small piece of ... Voir le document complet

... tasks for text, recent comparisons have conrmed the advantage of CNNs over RNNs when the task at hand is mostly a keyphrase recognition task [Yin ...frequential based meth- ods such as ...observed, ... Voir le document complet

... neural architectures demand new neuron models and learning ...simulations for integrating the stimuli and releasing discriminative spike patterns according to the adaptive filters associated with ... Voir le document complet

... in deep networks with many ...even for deep networks, the gradi- ents can be discretized sufficiently well into spikes if the gradient is properly ...As for the forward pass, ... Voir le document complet

... in deep learning from probability distributions suc- cessfully achieve classification or regression from distribution samples, thus invariant under permutation of the ...neural architectures to ... Voir le document complet

... INTRODUCTION Deep learning for computer vision grows more popular every year, especially thanks to Convolutional Neural Networks (CNN) that are able to learn powerful and expressive ... Voir le document complet

... Neural Networks (CNN), such as LeNet-5 [40] in arti- ficial vision tasks like hand-written digit classification or object recognition, stems from their architecture and inherent ...prior based on our ... Voir le document complet

... methods for predicting air ...machine learning (ML). A further reason for the wide use of these techniques is their capacities to formulate the non-linearity air pollution constraints, entirely ... Voir le document complet

... (corner based), SIFT [18] and SURF [54] (blob ...computationally efficient scheme made up of a fast multi-scale detector and a binary ...computationally efficient blob type detector mainly ... Voir le document complet

... of deep neural networks (deep learning) achieved considerable success in pattern recognition and text ...of deep learning on images, video or text classification, the application ... Voir le document complet

... tool for the discovery and characterisation of ...baselines. For this, we propose multitask autoencoders, including a domain-adaptive model used to construct domain-invariant fea- ture representations ... Voir le document complet