1 code implementation • 1 Dec 2023 • Pietro Bonazzi, Sizhen Bian, Giovanni Lippolis, Yawei Li, Sadique Sheik, Michele Magno
This paper introduces a neuromorphic methodology for eye tracking, harnessing pure event data captured by a Dynamic Vision Sensor (DVS) camera.
Ranked #1 on Pupil Detection on INI-30
1 code implementation • 24 Nov 2023 • Jens E. Pedersen, Steven Abreu, Matthias Jobst, Gregor Lenz, Vittorio Fra, Felix C. Bauer, Dylan R. Muir, Peng Zhou, Bernhard Vogginger, Kade Heckel, Gianvito Urgese, Sadasivan Shankar, Terrence C. Stewart, Jason K. Eshraghian, Sadique Sheik
Despite a well-established mathematical foundation for neural dynamics, the implementation details vary greatly across different platforms.
1 code implementation • 28 Sep 2023 • Gregor Lenz, Garrick Orchard, Sadique Sheik
We propose a temporal ANN-to-SNN conversion method, which we call Quartz, that is based on the time to first spike (TTFS).
no code implementations • 13 Apr 2023 • Ole Richter, Yannan Xing, Michele De Marchi, Carsten Nielsen, Merkourios Katsimpris, Roberto Cattaneo, Yudi Ren, Qian Liu, Sadique Sheik, Tugba Demirci, Ning Qiao
This is due to the increasing number of smart devices that require sensory processing for their application on the edge.
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.
1 code implementation • 20 May 2022 • Felix Christian Bauer, Gregor Lenz, Saeid Haghighatshoar, Sadique Sheik
In this paper, (i) we modify SLAYER and design an algorithm called EXODUS, that accounts for the neuron reset mechanism and applies the Implicit Function Theorem (IFT) to calculate the correct gradients (equivalent to those computed by BPTT), (ii) we eliminate the need for ad-hoc scaling of gradients, thus, reducing the training complexity tremendously, (iii) we demonstrate, via computer simulations, that EXODUS is numerically stable and achieves a comparable or better performance than SLAYER especially in various tasks with SNNs that rely on temporal features.
no code implementations • 2 Nov 2021 • Philipp Weidel, Sadique Sheik
We extend this idea to WaveSense, a spiking neural network inspired by the WaveNet architecture.
1 code implementation • 6 Oct 2021 • Julian Büchel, Gregor Lenz, Yalun Hu, Sadique Sheik, Martino Sorbaro
Event-based dynamic vision sensors provide very sparse output in the form of spikes, which makes them suitable for low-power applications.
2 code implementations • 3 Dec 2019 • Martino Sorbaro, Qian Liu, Massimo Bortone, Sadique Sheik
We demonstrate first that quantization-aware training of CNNs leads to better accuracy in SNNs.
no code implementations • 29 Sep 2017 • Georgios Detorakis, Sadique Sheik, Charles Augustine, Somnath Paul, Bruno U. Pedroni, Nikil Dutt, Jeffrey Krichmar, Gert Cauwenberghs, Emre Neftci
Embedded, continual learning for autonomous and adaptive behavior is a key application of neuromorphic hardware.
no code implementations • 15 Jun 2017 • Hesham Mostafa, Bruno Pedroni, Sadique Sheik, Gert Cauwenberghs
In this paper, we describe a hardware-efficient on-line learning technique for feedforward multi-layer ANNs that is based on pipelined backpropagation.
no code implementations • 5 Jan 2017 • Sadique Sheik, Somnath Paul, Charles Augustine, Gert Cauwenberghs
Several learning rules for synaptic plasticity, that depend on either spike timing or internal state variables, have been proposed in the past imparting varying computational capabilities to Spiking Neural Networks.
no code implementations • 11 Jul 2016 • Bruno U. Pedroni, Sadique Sheik, Siddharth Joshi, Georgios Detorakis, Somnath Paul, Charles Augustine, Emre Neftci, Gert Cauwenberghs
We present a novel method for realizing both causal and acausal weight updates using only forward lookup access of the synaptic connectivity table, permitting memory-efficient implementation.