no code implementations • ICCV 2023 • Yunqiang Li, Jan C. van Gemert, Torsten Hoefler, Bert Moons, Evangelos Eleftheriou, Bram-Ernst Verhoef
Deep learning algorithms are increasingly employed at the edge.
no code implementations • 28 Sep 2022 • Stanisław Woźniak, Hlynur Jónsson, Giovanni Cherubini, Angeliki Pantazi, Evangelos Eleftheriou
Visual oddity task was conceived as a universal ethnic-independent analytic intelligence test for humans.
no code implementations • 4 Oct 2021 • Thomas Bohnstingl, Ayush Garg, Stanisław Woźniak, George Saon, Evangelos Eleftheriou, Angeliki Pantazi
Automatic speech recognition (ASR) is a capability which enables a program to process human speech into a written form.
Automatic Speech Recognition Automatic Speech Recognition (ASR) +1
no code implementations • 23 Apr 2021 • Giorgia Dellaferrera, Stanislaw Wozniak, Giacomo Indiveri, Angeliki Pantazi, Evangelos Eleftheriou
Here, we propose a novel biologically inspired optimizer for artificial (ANNs) and spiking neural networks (SNNs) that incorporates key principles of synaptic integration observed in dendrites of cortical neurons: GRAPES (Group Responsibility for Adjusting the Propagation of Error Signals).
no code implementations • 15 Sep 2020 • Timoleon Moraitis, Abu Sebastian, Evangelos Eleftheriou
Biological neurons and their in-silico emulations for neuromorphic artificial intelligence (AI) use extraordinarily energy-efficient mechanisms, such as spike-based communication and local synaptic plasticity.
1 code implementation • 24 Jul 2020 • Thomas Bohnstingl, Stanisław Woźniak, Wolfgang Maass, Angeliki Pantazi, Evangelos Eleftheriou
For shallow networks, OSTL is gradient-equivalent to BPTT enabling for the first time online training of SNNs with BPTT-equivalent gradients.
no code implementations • 25 Mar 2020 • Vinay Joshi, Geethan Karunaratne, Manuel Le Gallo, Irem Boybat, Christophe Piveteau, Abu Sebastian, Bipin Rajendran, Evangelos Eleftheriou
Strategies to improve the efficiency of MVM computation in hardware have been demonstrated with minimal impact on training accuracy.
1 code implementation • 5 Mar 2020 • Kornilios Kourtis, Martino Dazzi, Nikolas Ioannou, Tobias Grosser, Abu Sebastian, Evangelos Eleftheriou
Computational memory (CM) is a promising approach for accelerating inference on neural networks (NN) by using enhanced memories that, in addition to storing data, allow computations on them.
no code implementations • 8 Jun 2019 • Martino Dazzi, Abu Sebastian, Pier Andrea Francese, Thomas Parnell, Luca Benini, Evangelos Eleftheriou
We show that this communication fabric facilitates the pipelined execution of all state of-the-art CNNs by proving the existence of a homomorphism between one graph representation of these networks and the proposed graph topology.
no code implementations • 7 Jun 2019 • Vinay Joshi, Manuel Le Gallo, Irem Boybat, Simon Haefeli, Christophe Piveteau, Martino Dazzi, Bipin Rajendran, Abu Sebastian, Evangelos Eleftheriou
In-memory computing is a promising non-von Neumann approach where certain computational tasks are performed within memory units by exploiting the physical attributes of memory devices.
Emerging Technologies
no code implementations • 28 May 2019 • S. R. Nandakumar, Irem Boybat, Manuel Le Gallo, Evangelos Eleftheriou, Abu Sebastian, Bipin Rajendran
Combining the computational potential of supervised SNNs with the parallel compute power of computational memory, the work paves the way for next-generation of efficient brain-inspired systems.
no code implementations • 11 Jan 2019 • Bipin Rajendran, Abu Sebastian, Michael Schmuker, Narayan Srinivasa, Evangelos Eleftheriou
In this paper, we review some of the architectural and system level design aspects involved in developing a new class of brain-inspired information processing engines that mimic the time-based information encoding and processing aspects of the brain.
1 code implementation • 17 Dec 2018 • Stanisław Woźniak, Angeliki Pantazi, Thomas Bohnstingl, Evangelos Eleftheriou
Neural networks have become the key technology of artificial intelligence and have contributed to breakthroughs in several machine learning tasks, primarily owing to advances in deep learning applied to Artificial Neural Networks (ANNs).
no code implementations • 17 Jun 2017 • Timoleon Moraitis, Abu Sebastian, Irem Boybat, Manuel Le Gallo, Tomas Tuma, Evangelos Eleftheriou
However, some spike-timing-related strengths of SNNs are hindered by the sensitivity of spike-timing-dependent plasticity (STDP) rules to input spike rates, as fine temporal correlations may be obstructed by coarser correlations between firing rates.
no code implementations • 16 Jan 2017 • Manuel Le Gallo, Abu Sebastian, Roland Mathis, Matteo Manica, Heiner Giefers, Tomas Tuma, Costas Bekas, Alessandro Curioni, Evangelos Eleftheriou
As CMOS scaling reaches its technological limits, a radical departure from traditional von Neumann systems, which involve separate processing and memory units, is needed in order to significantly extend the performance of today's computers.
Emerging Technologies