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Deep Neural Networks for the Recognition and Classification of Heart Murmurs Using Neuromorphic Auditory Sensors [Article]
(IEEE Computer Society, 2017)
Auscultation is one of the most used techniques for detecting cardiovascular diseases, which is one of the main causes of death in the world. Heart murmurs are the most common abnormal finding when a patient visits the ...
Deep Spiking Neural Network model for time-variant signals classification: a real-time speech recognition approach [Presentation]
(IEEE Computer Society, 2018)
Speech recognition has become an important task to improve the human-machine interface. Taking into account the limitations of current automatic speech recognition systems, like non-real time cloud-based solutions or ...
Event-based Row-by-Row Multi-convolution engine for Dynamic-Vision Feature Extraction on FPGA [Presentation]
(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 ...
A Protocol Generator Tool for Automatic In-Vitro HPV Robotic Analysis [Presentation]
(Springer, 2017)
Human Papilloma Virus (HPV) could develop precancerous lesions and invasive cancer, as it is the main cause of nearly all cases of cervical cancer. There are many strains of HPV and current vaccines can only protect ...
NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Maps [Article]
(IEEE Computer Society, 2019)
Convolutional neural networks (CNNs) have become the dominant neural network architecture for solving many stateof- the-art (SOA) visual processing tasks. Even though Graphical Processing Units (GPUs) are most often ...
Comprehensive Evaluation of OpenCL-based Convolutional Neural Network Accelerators in Xilinx and Altera FPGAs [Article]
(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 ...
Comprehensive Evaluation of OpenCL-Based CNN Implementations for FPGAs [Presentation]
(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 ...
Asynchronous Spiking Neurons, the Natural Key to Exploit Temporal Sparsity [Article]
(IEEE Computer Society, 2019)
Inference of Deep Neural Networks for stream signal (Video/Audio) processing in edge devices is still challenging. Unlike the most state of the art inference engines which are efficient for static signals, our brain is ...
ConvNets Experiments on SpiNNaker [Presentation]
(IEEE Computer Society, 2015)
The SpiNNaker Hardware platform allows emulating generic neural network topologies, where each neuronto- neuron connection is defined by an independent synaptic weight. Consequently, weight storage requires an ...
Neuromorphic LIF Row-by-Row Multiconvolution Processor for FPGA [Article]
(IEEE Computer Society, 2018)
Deep Learning algorithms have become state-of-theart methods for multiple fields, including computer vision, speech recognition, natural language processing, and audio recognition, among others. In image vision, ...