no code implementations • 30 Apr 2024 • Lorenzo Pes, Rick Luiken, Federico Corradi, Charlotte Frenkel
While the human brain efficiently adapts to new tasks from a continuous stream of information, neural network models struggle to learn from sequential information without catastrophically forgetting previously learned tasks.
no code implementations • 16 Apr 2024 • Alberto Patino-Saucedo, Roy Meijer, Amirreza Yousefzadeh, Manil-Dev Gomony, Federico Corradi, Paul Detteter, Laura Garrido-Regife, Bernabe Linares-Barranco, Manolis Sifalakis
In this work, we propose a framework to train and deploy, in digital neuromorphic hardware, highly performing spiking neural network models (SNNs) where apart from the synaptic weights, the per-synapse delays are also co-optimized.
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 • 20 Dec 2021 • Bojian Yin, Federico Corradi, Sander M. Bohte
When combined with a novel dynamic spiking neuron model, the Liquid-Time-Constant neuron, we show that SNNs trained with FPTT outperform online BPTT approximations, and approach or exceed offline BPTT accuracy on temporal classification tasks.
no code implementations • 23 Nov 2021 • M. Lakshmi Varshika, Adarsha Balaji, Federico Corradi, Anup Das, Jan Stuijt, Francky Catthoor
We propose a system software framework called SentryOS to map SDCNN inference applications to the proposed design.
1 code implementation • 1 Oct 2021 • Marina González-Álvarez, Julien Dupeyroux, Federico Corradi, Guido de Croon
Robotic airships offer significant advantages in terms of safety, mobility, and extended flight times.
no code implementations • 12 Mar 2021 • 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 models of neural computation, and also with the aim of extracting biological neurons' energy efficiency; the performance of such networks however has remained lacking compared to classical artificial neural networks (ANNs).
Ranked #5 on Audio Classification on SSC
1 code implementation • 24 May 2020 • 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 hardware.
no code implementations • 2 Jul 2018 • Diederik Paul Moeys, Daniel Neil, Federico Corradi, Emmett Kerr, Philip Vance, Gautham Das, Sonya A. Coleman, Thomas M. McGinnity, Dermot Kerr, Tobi Delbruck
Conventional vision CNNs are driven by camera frames at constant sample rate, thus achieving a fixed latency and power consumption tradeoff.
no code implementations • 5 Jun 2017 • Alessandro Aimar, Hesham Mostafa, Enrico Calabrese, Antonio Rios-Navarro, Ricardo Tapiador-Morales, Iulia-Alexandra Lungu, Moritz B. Milde, Federico Corradi, Alejandro Linares-Barranco, Shih-Chii Liu, Tobi Delbruck
By exploiting sparsity, NullHop achieves an efficiency of 368%, maintains over 98% utilization of the MAC units, and achieves a power efficiency of over 3TOp/s/W in a core area of 6. 3mm$^2$.
no code implementations • 30 Jun 2016 • Diederik Paul Moeys, Federico Corradi, Emmett Kerr, Philip Vance, Gautham Das, Daniel Neil, Dermot Kerr, Tobi Delbruck
The CNN is trained and run on data from a Dynamic and Active Pixel Sensor (DAVIS) mounted on a Summit XL robot (the predator), which follows another one (the prey).
no code implementations • 17 Jun 2015 • Massimiliano Giulioni, Federico Corradi, Vittorio Dante, Paolo del Giudice
The initial network state is dictated by the stimulus, and relaxation to the attractor state implements the retrieval of the corresponding memorized prototypical pattern.