Search Results for author: Lyes Khacef

Found 14 papers, 5 papers with code

TDE-3: An improved prior for optical flow computation in spiking neural networks

no code implementations18 Feb 2024 Matthew Yedutenko, Federico Paredes-Valles, Lyes Khacef, Guido C. H. E. de Croon

Using synthetic data we compared training and inference with spike count and ISI with respect to changes in stimuli dynamic range, spatial frequency, and level of noise.

Motion Detection Navigate +1

Low-power event-based face detection with asynchronous neuromorphic hardware

no code implementations21 Dec 2023 Caterina Caccavella, Federico Paredes-Vallés, Marco Cannici, Lyes Khacef

We show that the power consumption of the chip is directly proportional to the number of synaptic operations in the spiking neural network, and we explore the trade-off between power consumption and detection precision with different firing rate regularization, achieving an on-chip face detection mAP[0. 5] of ~0. 6 while consuming only ~20 mW.

Face Detection object-detection +1

NeuroBench: A Framework for Benchmarking Neuromorphic Computing Algorithms and Systems

1 code implementation10 Apr 2023 Jason Yik, Korneel Van den Berghe, Douwe den Blanken, Younes Bouhadjar, Maxime Fabre, Paul Hueber, Denis Kleyko, Noah Pacik-Nelson, Pao-Sheng Vincent Sun, Guangzhi Tang, Shenqi Wang, Biyan Zhou, Soikat Hasan Ahmed, George Vathakkattil Joseph, Benedetto Leto, Aurora Micheli, Anurag Kumar Mishra, Gregor Lenz, Tao Sun, Zergham Ahmed, Mahmoud Akl, Brian Anderson, Andreas G. Andreou, Chiara Bartolozzi, Arindam Basu, Petrut Bogdan, Sander Bohte, Sonia Buckley, Gert Cauwenberghs, Elisabetta Chicca, Federico Corradi, Guido de Croon, Andreea Danielescu, Anurag Daram, Mike Davies, Yigit Demirag, Jason Eshraghian, Tobias Fischer, Jeremy Forest, Vittorio Fra, Steve Furber, P. Michael Furlong, William Gilpin, Aditya Gilra, Hector A. Gonzalez, Giacomo Indiveri, Siddharth Joshi, Vedant Karia, Lyes Khacef, James C. Knight, Laura Kriener, Rajkumar Kubendran, Dhireesha Kudithipudi, Yao-Hong Liu, Shih-Chii Liu, Haoyuan Ma, Rajit Manohar, Josep Maria Margarit-Taulé, Christian Mayr, Konstantinos Michmizos, Dylan Muir, Emre Neftci, Thomas Nowotny, Fabrizio Ottati, Ayca Ozcelikkale, Priyadarshini Panda, Jongkil Park, Melika Payvand, Christian Pehle, Mihai A. Petrovici, Alessandro Pierro, Christoph Posch, Alpha Renner, Yulia Sandamirskaya, Clemens JS Schaefer, André van Schaik, Johannes Schemmel, Samuel Schmidgall, Catherine Schuman, Jae-sun Seo, Sadique Sheik, Sumit Bam Shrestha, Manolis Sifalakis, Amos Sironi, Matthew Stewart, Kenneth Stewart, Terrence C. Stewart, Philipp Stratmann, Jonathan Timcheck, Nergis Tömen, Gianvito Urgese, Marian Verhelst, Craig M. Vineyard, Bernhard Vogginger, Amirreza Yousefzadeh, Fatima Tuz Zohora, Charlotte Frenkel, Vijay Janapa Reddi

The NeuroBench framework introduces a common set of tools and systematic methodology for inclusive benchmark measurement, delivering an objective reference framework for quantifying neuromorphic approaches in both hardware-independent (algorithm track) and hardware-dependent (system track) settings.


A Comparison of Temporal Encoders for Neuromorphic Keyword Spotting with Few Neurons

no code implementations24 Jan 2023 Mattias Nilsson, Ton Juny Pina, Lyes Khacef, Foteini Liwicki, Elisabetta Chicca, Fredrik Sandin

With the expansion of AI-powered virtual assistants, there is a need for low-power keyword spotting systems providing a "wake-up" mechanism for subsequent computationally expensive speech recognition.

Binary Classification Keyword Spotting +2

ETLP: Event-based Three-factor Local Plasticity for online learning with neuromorphic hardware

1 code implementation19 Jan 2023 Fernando M. Quintana, Fernando Perez-Peña, Pedro L. Galindo, Emre O. Neftci, Elisabetta Chicca, Lyes Khacef

We also show that when using local plasticity, threshold adaptation in spiking neurons and a recurrent topology are necessary to learn spatio-temporal patterns with a rich temporal structure.

Impact of spiking neurons leakages and network recurrences on event-based spatio-temporal pattern recognition

no code implementations14 Nov 2022 Mohamed Sadek Bouanane, Dalila Cherifi, Elisabetta Chicca, Lyes Khacef

Spiking neural networks coupled with neuromorphic hardware and event-based sensors are getting increased interest for low-latency and low-power inference at the edge.

Spike-based local synaptic plasticity: A survey of computational models and neuromorphic circuits

no code implementations30 Sep 2022 Lyes Khacef, Philipp Klein, Matteo Cartiglia, Arianna Rubino, Giacomo Indiveri, Elisabetta Chicca

To this end, in this survey, we provide a comprehensive overview of representative brain-inspired synaptic plasticity models and mixed-signal CMOS neuromorphic circuits within a unified framework.

A unified software/hardware scalable architecture for brain-inspired computing based on self-organizing neural models

no code implementations6 Jan 2022 Artem R. Muliukov, Laurent Rodriguez, Benoit Miramond, Lyes Khacef, Joachim Schmidt, Quentin Berthet, Andres Upegui

This work also demonstrates the distributed and scalable nature of the model through both simulation results and hardware execution on a dedicated FPGA-based platform named SCALP (Self-configurable 3D Cellular Adaptive Platform).

Multimodal Association

Improving Self-Organizing Maps with Unsupervised Feature Extraction

1 code implementation4 Sep 2020 Lyes Khacef, Laurent Rodriguez, Benoit Miramond

We conduct a comparative study on the SOM classification accuracy with unsupervised feature extraction using two different approaches: a machine learning approach with Sparse Convolutional Auto-Encoders using gradient-based learning, and a neuroscience approach with Spiking Neural Networks using Spike Timing Dependant Plasticity learning.

Classification General Classification +2

GPU-based Self-Organizing Maps for Post-Labeled Few-Shot Unsupervised Learning

1 code implementation4 Sep 2020 Lyes Khacef, Vincent Gripon, Benoit Miramond

In this work, we consider the problem of post-labeled few-shot unsupervised learning, a classification task where representations are learned in an unsupervised fashion, to be later labeled using very few annotated examples.

Classification Clustering +4

Brain-inspired self-organization with cellular neuromorphic computing for multimodal unsupervised learning

no code implementations11 Apr 2020 Lyes Khacef, Laurent Rodriguez, Benoit Miramond

The divergence mechanism is used to label one modality based on the other, while the convergence mechanism is used to improve the overall accuracy of the system.

Distributed Computing Multimodal Association

Sensor fusion using EMG and vision for hand gesture classification in mobile applications

no code implementations19 Oct 2019 Enea Ceolini, Gemma Taverni, Lyes Khacef, Melika Payvand, Elisa Donati

The discrimination of human gestures using wearable solutions is extremely important as a supporting technique for assisted living, healthcare of the elderly and neurorehabilitation.

Electromyography (EMG) General Classification +3

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