no code implementations • 20 Oct 2023 • Evie Andrew, Travis Monk, André van Schaik
This article introduces the Event based Prediction Suffix Tree (EPST), a biologically inspired, event-based prediction algorithm.
no code implementations • 11 Oct 2023 • Shavika Rastogi, Nik Dennler, Michael Schmuker, André van Schaik
We show that in the setting of controlled airflow-embedded gas injections, the time difference between the two generated pulses varies inversely with gas concentration, which is in agreement with the spike timing difference between tufted cells and mitral cells of the mammalian olfactory bulb.
1 code implementation • 20 Sep 2023 • Nik Dennler, André van Schaik, Michael Schmuker
Neuromorphic computing is one of the few current approaches that have the potential to significantly reduce power consumption in Machine Learning and Artificial Intelligence.
no code implementations • 10 Jun 2023 • Adithya Krishna, Srikanth Rohit Nudurupati, Chandana D G, Pritesh Dwivedi, André van Schaik, Mahesh Mehendale, Chetan Singh Thakur
In this paper, we present RAMAN, a Re-configurable and spArse tinyML Accelerator for infereNce on edge, architected to exploit the sparsity to reduce area (storage), power as well as latency.
no code implementations • 31 May 2023 • Ali Mehrabi, Yeshwanth Bethi, André van Schaik, Andrew Wabnitz, Saeed Afshar
This paper presents an efficient hardware implementation of the recently proposed Optimized Deep Event-driven Spiking Neural Network Architecture (ODESA).
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 • 14 Dec 2022 • Ying Xu, Samalika Perera, Yeshwanth Bethi, Saeed Afshar, André van Schaik
This paper presents a reconfigurable digital implementation of an event-based binaural cochlear system on a Field Programmable Gate Array (FPGA).
1 code implementation • 17 Nov 2022 • Nicholas Owen Ralph, Alexandre Marcireau, Saeed Afshar, Nicholas Tothill, André van Schaik, Gregory Cohen
These techniques are vital in building event-based space imaging systems capable of real-world space situational awareness tasks.
1 code implementation • IEEE Access 2022 • Yeshwanth Bethi, Ying Xu, Gregory Cohen, André van Schaik, and Saeed Afshar
Through the use of simple local adaptive selection thresholds at each node, the network rapidly learns to appropriately allocate its neuronal resources at each layer for any given problem without using an error measure.
no code implementations • 19 Aug 2021 • Nik Dennler, Shavika Rastogi, Jordi Fonollosa, André van Schaik, Michael Schmuker
Metal oxide (MOx) electro-chemical gas sensors are a sensible choice for many applications, due to their tunable sensitivity, their space-efficiency and their low price.
no code implementations • 13 May 2021 • Weng Fei Low, Ankit Sonthalia, Zhi Gao, André van Schaik, Bharath Ramesh
Most successful computer vision models transform low-level features, such as Gabor filter responses, into richer representations of intermediate or mid-level complexity for downstream visual tasks.
no code implementations • 18 Jul 2019 • Saeed Afshar, Ying Xu, Jonathan Tapson, André van Schaik, Gregory Cohen
A novel heuristic method for network size selection is proposed which makes use of noise events and their feature representations.
no code implementations • 23 May 2018 • Chetan Singh Thakur, Jamal Molin, Gert Cauwenberghs, Giacomo Indiveri, Kundan Kumar, Ning Qiao, Johannes Schemmel, Runchun Wang, Elisabetta Chicca, Jennifer Olson Hasler, Jae-sun Seo, Shimeng Yu, Yu Cao, André van Schaik, Ralph Etienne-Cummings
Neuromorphic engineering (NE) encompasses a diverse range of approaches to information processing that are inspired by neurobiological systems, and this feature distinguishes neuromorphic systems from conventional computing systems.
31 code implementations • 17 Feb 2017 • Gregory Cohen, Saeed Afshar, Jonathan Tapson, André van Schaik
The MNIST dataset has become a standard benchmark for learning, classification and computer vision systems.
Ranked #6 on Image Classification on EMNIST-Digits
no code implementations • 13 Mar 2016 • Runchun Wang, Chetan Singh Thakur, Tara Julia Hamilton, Jonathan Tapson, André van Schaik
The decay generator will then generate an exponential decay, which will be used by the STDP adaptor to perform the weight adaption.
no code implementations • 2 Mar 2015 • Chetan Singh Thakur, Tara Julia Hamilton, Runchun Wang, Jonathan Tapson, André van Schaik
These neuronal populations are characterised by a diverse distribution of tuning curves, ensuring that the entire range of input stimuli is encoded.
no code implementations • 2 Mar 2015 • Chetan Singh Thakur, Tara Julia Hamilton, Jonathan Tapson, Richard F. Lyon, André van Schaik
Here, we implement the Cascade of Asymmetric Resonators (CAR) model of the cochlea on an FPGA.
no code implementations • 29 Dec 2014 • Mark D. McDonnell, Migel D. Tissera, Tony Vladusich, André van Schaik, Jonathan Tapson
Our close to state-of-the-art results for MNIST and NORB suggest that the ease of use and accuracy of the ELM algorithm for designing a single-hidden-layer neural network classifier should cause it to be given greater consideration either as a standalone method for simpler problems, or as the final classification stage in deep neural networks applied to more difficult problems.
no code implementations • 12 Jun 2014 • Philip de Chazal, Jonathan Tapson, André van Schaik
We present an alternative to the pseudo-inverse method for determining the hidden to output weight values for Extreme Learning Machines performing classification tasks.
no code implementations • 11 Jun 2014 • Jonathan Tapson, Philip de Chazal, André van Schaik
In the absence of supervised training for the input weights, random linear combinations of training data samples are used to project the input data to a higher dimensional hidden layer.
no code implementations • 30 May 2014 • André van Schaik, Jonathan Tapson
The ELM method has become widely used for classification and regressions problems as a result of its accuracy, simplicity and ease of use.
no code implementations • 30 May 2014 • Jonathan Tapson, André van Schaik
The modifications involve the re-definition of hidden layer units as synaptic kernels, in which the input delta functions are transformed into continuous-valued signals using a variety of impulse-response functions.