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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. ...
Artículo
Efficient Memory Organization for DNN Hardware Accelerator Implementation on PSoC
(MDPI, 2021-01)
The use of deep learning solutions in different disciplines is increasing and their algorithms are computationally expensive in most cases. For this reason, numerous hardware accelerators have appeared to compute their ...
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
Interfacing PDM MEMS Microphones with PFM Spiking Systems: Application for Neuromorphic Auditory Sensors
(Springer, 2022)
Neuromorphic computation processes sensors output in the spiking domain, which presents constraints in many cases when converting information to spikes, loosing, as example, temporal accuracy. This paper presents a spike-based ...
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. ...
Ponencia
A 20Mevps/32Mev event-based USB framework for neuromorphic systems debugging
(IEEE Computer Society, 2016)
Neuromorphic systems are engineering solutions that take inspiration from biological neural systems. They use spike-or event-based representation and codification of the information. This codification allows performing ...
Artículo
NeuroPod: a real-time neuromorphic spiking CPG applied to robotics
(Elsevier, 2020)
Initially, robots were developed with the aim of making our life easier, carrying out repetitive or dangerous tasks for humans. Although they were able to perform these tasks, the latest generation of robots are being ...
Artículo
Stereo Matching in Address-Event-Representation (AER) Bio-Inspired Binocular Systems in a Field-Programmable Gate Array (FPGA)
(MDPI, 2019)
In stereo-vision processing, the image-matching step is essential for results, although it involves a very high computational cost. Moreover, the more information is processed, the more time is spent by the matching ...
Ponencia
Integrating a hippocampus memory model into a neuromorphic robotic-arm for trajectory navigation
(IEEE Circuits and Systems Society., 2024)
Neuromorphic engineering endeavors to integrate the computational prowess and efficiency inherent in biological neuronal systems, such as the brain, into contemporary technological systems, primarily through the deployment ...