no code implementations • 18 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.
no code implementations • 21 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.
1 code implementation • 10 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.
no code implementations • 24 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.
1 code implementation • 19 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.
no code implementations • 14 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.
no code implementations • 30 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.
2 code implementations • 30 May 2022 • Simon F Muller-Cleve, Vittorio Fra, Lyes Khacef, Alejandro Pequeno-Zurro, Daniel Klepatsch, Evelina Forno, Diego G Ivanovich, Shavika Rastogi, Gianvito Urgese, Friedemann Zenke, Chiara Bartolozzi
Spatio-temporal pattern recognition is a fundamental ability of the brain which is required for numerous real-world activities.
no code implementations • 6 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).
1 code implementation • 4 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.
Ranked #5 on Unsupervised MNIST on MNIST
1 code implementation • 4 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.
no code implementations • 11 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.
no code implementations • 19 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.
no code implementations • 30 Oct 2018 • Lyes Khacef, Bernard Girau, Nicolas Rougier, Andres Upegui, Benoit Miramond
This paper presents the self-organized neuromorphic architecture named SOMA.