298 citations · 341 across the 11 of their papers we have counts for
11 papers
Adaptive-Frequency Resonate-and-Fire Neurons for Spectral Estimation of Streaming Radar Signals
Stefano Chiavazza, Sen Yuan, Marc Geilen +2
Frequency Modulated Continuous Wave (FMCW) radar systems traditionally rely on Fourier-based methods, such as the Fast Fourier Transform (FFT), to estimate target range and velocit…
Empirical study on the efficiency of Spiking Neural Networks with axonal delays, and algorithm-hardware benchmarking
Alberto Patiño-Saucedo, Amirreza Yousefzadeh, Guangzhi Tang +3
The role of axonal synaptic delays in the efficacy and performance of artificial neural networks has been largely unexplored. In step-based analog-valued neural network models (ANN…
Accurate online training of dynamical spiking neural networks through Forward Propagation Through Time
Bojian Yin, Federico Corradi, Sander M. Bohte
The event-driven and sparse nature of communication between spiking neurons in the brain holds great promise for flexible and energy-efficient AI. Recent advances in learning algor…
Design of Many-Core Big Little μBrain for Energy-Efficient Embedded Neuromorphic Computing
M. Lakshmi Varshika, Adarsha Balaji, Federico Corradi +3
As spiking-based deep learning inference applications are increasing in embedded systems, these systems tend to integrate neuromorphic accelerators such as Brain to improve ener…
Evolved neuromorphic radar-based altitude controller for an autonomous open-source blimp
Marina González-Álvarez, Julien Dupeyroux, Federico Corradi +1
Robotic airships offer significant advantages in terms of safety, mobility, and extended flight times. However, their highly restrictive weight constraints pose a major challenge r…
Accurate and efficient time-domain classification with adaptive spiking recurrent neural networks
Bojian Yin, Federico Corradi, Sander M. Bohte
Inspired by more detailed modeling of biological neurons, Spiking neural networks (SNNs) have been investigated both as more biologically plausible and potentially more powerful mo…
Effective and Efficient Computation with Multiple-timescale Spiking Recurrent Neural Networks
Bojian Yin, Federico Corradi, Sander M. Bohté
The emergence of brain-inspired neuromorphic computing as a paradigm for edge AI is motivating the search for high-performance and efficient spiking neural networks to run on this…
PRED18: Dataset and Further Experiments with DAVIS Event Camera in Predator-Prey Robot Chasing
Diederik Paul Moeys, Daniel Neil, Federico Corradi +7
Machine vision systems using convolutional neural networks (CNNs) for robotic applications are increasingly being developed. Conventional vision CNNs are driven by camera frames at…
NullHop: A Flexible Convolutional Neural Network Accelerator Based on Sparse Representations of Feature Maps
Alessandro Aimar, Hesham Mostafa, Enrico Calabrese +8
Convolutional neural networks (CNNs) have become the dominant neural network architecture for solving many state-of-the-art (SOA) visual processing tasks. Even though Graphical Pro…
Steering a Predator Robot using a Mixed Frame/Event-Driven Convolutional Neural Network
Diederik Paul Moeys, Federico Corradi, Emmett Kerr +5
This paper describes the application of a Convolutional Neural Network (CNN) in the context of a predator/prey scenario. The CNN is trained and run on data from a Dynamic and Activ…
Real time unsupervised learning of visual stimuli in neuromorphic VLSI systems
Massimiliano Giulioni, Federico Corradi, Vittorio Dante +1
Neuromorphic chips embody computational principles operating in the nervous system, into microelectronic devices. In this domain it is important to identify computational primitive…