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Mostrando ítems 1-10 de 23
Ponencia
Work-in-Progress: A Neuromorphic Approach of the Sound Source Localization Task in Real-Time Embedded Systems
(ACM Digital Library, 2019)
Autonomous robots have become a very popular topic within the artificial intelligence field. These systems are able to perform difficult or risky tasks that could be dangerous when done by humans or trained animals. ...
Ponencia
Live Demonstration: Neuromorphic Row-by-Row Multi-convolution FPGA Processor-SpiNNaker architecture for Dynamic-Vision Feature Extraction
(IEEE Computer Society, 2019)
In this demonstration a spiking neural network architecture for vision recognition using an FPGA spiking convolution processor, based on leaky integrate and fire neurons (LIF) and a SpiNNaker board is presented. The ...
Artículo
Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs
(Cornell University, 2016)
Deep learning has significantly advanced the state of the art in artificial intelligence, gaining wide popularity from both industry and academia. Special interest is around Convolutional Neural Networks (CNN), which take ...
Tesis Doctoral
Neuromorphic deep convolutional neural network learning systems for FPGA in real time
(2019-12-13)
Deep Learning algorithms have become one of the best approaches for pattern recognition in several fields, including computer vision, speech recognition, natural language processing, and audio recognition, among others. ...
Ponencia
Comprehensive Evaluation of OpenCL-Based CNN Implementations for FPGAs
(Springer, 2017)
Deep learning has significantly advanced the state of the art in artificial intelligence, gaining wide popularity from both industry and academia. Special interest is around Convolutional Neural Networks (CNN), which ...
Artículo
Embedded neural network for real-time animal behavior classification
(Elsevier, 2018)
Recent biological studies have focused on understanding animal interactions and welfare. To help biolo- gists to obtain animals’ behavior information, resources like wireless sensor networks are needed. More- over, large ...
Artículo
Dynamic Vision Sensor integration on FPGA-based CNN accelerators for high-speed visual classification
(Cornell University, 2019)
Deep-learning is a cutting edge theory that is being applied to many fields. For vision applications the Convolutional Neural Networks (CNN) are demanding significant accuracy for classification tasks. Numerous hardware ...
Ponencia
Live demonstration — Multilayer spiking neural network for audio samples classification using SpiNNaker
(IEEE Computer Society, 2017)
In this demonstration we present a spiking neural network architecture for audio samples classification using SpiNNaker. The network consists of different leaky integrate-and-fire neuron layers. The connections between ...
Ponencia
Event-based Row-by-Row Multi-convolution engine for Dynamic-Vision Feature Extraction on FPGA
(IEEE Computer Society, 2018)
Neural networks algorithms are commonly used to recognize patterns from different data sources such as audio or vision. In image recognition, Convolutional Neural Networks are one of the most effective techniques due ...
Ponencia
Spiking row-by-row FPGA Multi-kernel and Multi-layer Convolution Processor.
(IEEE Computer Society, 2019)
Spiking convolutional neural networks have become a novel approach for machine vision tasks, due to the latency to process an input stimulus from a scene, and the low power consumption of these kind of solutions. ...