no code implementations • 8 Jun 2023 • Ali Safa, Tim Verbelen, Lars Keuninckx, Ilja Ocket, André Bourdoux, Francky Catthoor, Georges Gielen, Gert Cauwenberghs
This work studies how brain-inspired neural ensembles equipped with local Hebbian plasticity can perform active inference (AIF) in order to control dynamical agents.
no code implementations • 21 Nov 2022 • Bert Herteleer, Anastasios Kladas, Gofran Chowdhury, Francky Catthoor, Jan Cappelle
We propose two thermal models, WM1 and WM2, and compare these against the models of Ross, Sandia, and Faiman on twenty-four datasets of fifteen sites, with time resolutions ranging from 1$~$s to 1$~$h, the majority of these at 1$~$min resolution.
no code implementations • 9 Oct 2022 • Ali Safa, Tim Verbelen, Ilja Ocket, André Bourdoux, Hichem Sahli, Francky Catthoor, Georges Gielen
This work proposes a first-of-its-kind SLAM architecture fusing an event-based camera and a Frequency Modulated Continuous Wave (FMCW) radar for drone navigation.
no code implementations • 16 Feb 2022 • Ali Safa, Ilja Ocket, André Bourdoux, Hichem Sahli, Francky Catthoor, Georges Gielen
This paper demonstrates for the first time that a biologically-plausible spiking neural network (SNN) equipped with Spike-Timing-Dependent Plasticity (STDP) can continuously learn to detect walking people on the fly using retina-inspired, event-based cameras.
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.
no code implementations • 1 Nov 2021 • Ali Safa, Ilja Ocket, André Bourdoux, Hichem Sahli, Francky Catthoor, Georges Gielen
We present new theoretical foundations for unsupervised Spike-Timing-Dependent Plasticity (STDP) learning in spiking neural networks (SNNs).
no code implementations • 28 Sep 2021 • Ali Safa, Tim Verbelen, Ilja Ocket, André Bourdoux, Francky Catthoor, Georges G. E. Gielen
Currently however, people detection systems used on drones are solely based on standard cameras besides an emerging number of works discussing the fusion of imaging and event-based cameras.
1 code implementation • 5 Aug 2021 • Ali Safa, André Bourdoux, Ilja Ocket, Francky Catthoor, Georges G. E. Gielen
Radar processing via spiking neural networks (SNNs) has recently emerged as a solution in the field of ultra-low-power wireless human-computer interaction.
no code implementations • 5 May 2021 • Shihao Song, Jui Hanamshet, Adarsha Balaji, Anup Das, Jeffrey L. Krichmar, Nikil D. Dutt, Nagarajan Kandasamy, Francky Catthoor
We propose a new architectural technique to mitigate the aging-related reliability problems in neuromorphic systems, by designing an intelligent run-time manager (NCRTM), which dynamically destresses neuron and synapse circuits in response to the short-term aging in their CMOS transistors during the execution of machine learning workloads, with the objective of meeting a reliability target.
no code implementations • 4 May 2021 • Adarsha Balaji, Shihao Song, Twisha Titirsha, Anup Das, Jeffrey Krichmar, Nikil Dutt, James Shackleford, Nagarajan Kandasamy, Francky Catthoor
Recently, both industry and academia have proposed many different neuromorphic architectures to execute applications that are designed with Spiking Neural Network (SNN).
no code implementations • 9 Mar 2021 • Twisha Titirsha, Shihao Song, Anup Das, Jeffrey Krichmar, Nikil Dutt, Nagarajan Kandasamy, Francky Catthoor
We propose eSpine, a novel technique to improve lifetime by incorporating the endurance variation within each crossbar in mapping machine learning workloads, ensuring that synapses with higher activation are always implemented on memristors with higher endurance, and vice versa.
no code implementations • 19 Sep 2020 • Adarsha Balaji, Shihao Song, Anup Das, Jeffrey Krichmar, Nikil Dutt, James Shackleford, Nagarajan Kandasamy, Francky Catthoor
With growing model complexity, mapping Spiking Neural Network (SNN)-based applications to tile-based neuromorphic hardware is becoming increasingly challenging.
no code implementations • 11 Jun 2020 • Adarsha Balaji, Thibaut Marty, Anup Das, Francky Catthoor
In this paper, we propose a design methodology to partition and map the neurons and synapses of online learning SNN-based applications to neuromorphic architectures at {run-time}.
1 code implementation • 21 Mar 2020 • Adarsha Balaji, Prathyusha Adiraju, Hirak J. Kashyap, Anup Das, Jeffrey L. Krichmar, Nikil D. Dutt, Francky Catthoor
We also use PyCARL to analyze these SNNs for a state-of-the-art neuromorphic hardware and demonstrate a significant performance deviation from software-only simulations.
no code implementations • 1 Nov 2019 • Adarsha Balaji, Shihao Song, Anup Das, Nikil Dutt, Jeff Krichmar, Nagarajan Kandasamy, Francky Catthoor
Our framework first extracts the precise times at which a charge pump in the hardware is activated to support neural computations within a workload.
no code implementations • 4 Sep 2019 • Adarsha Balaji, Anup Das, Yuefeng Wu, Khanh Huynh, Francesco Dell'Anna, Giacomo Indiveri, Jeffrey L. Krichmar, Nikil Dutt, Siebren Schaafsma, Francky Catthoor
SpiNePlacer then finds the best placement of local and global synapses on the hardware using a meta-heuristic-based approach to minimize energy consumption and spike latency.
no code implementations • 13 Aug 2019 • Anup Das, Yuefeng Wu, Khanh Huynh, Francesco Dell'Anna, Francky Catthoor, Siebren Schaafsma
Partitioning SNNs becomes essential in order to map them on neuromorphic hardware with the major aim to reduce the global communication latency and energy overhead.
no code implementations • 13 Aug 2019 • Anup Das, Francky Catthoor, Siebren Schaafsma
Heartbeat classification using electrocardiogram (ECG) data is a vital assistive technology for wearable health solutions.
no code implementations • 18 Jul 2017 • Anup Das, Paruthi Pradhapan, Willemijn Groenendaal, Prathyusha Adiraju, Raj Thilak Rajan, Francky Catthoor, Siebren Schaafsma, Jeffrey L. Krichmar, Nikil Dutt, Chris Van Hoof
The novelty of our approach lies in (1) encoding spatio-temporal properties of ECG signals directly into spike train and using this to excite recurrently connected spiking neurons in a Liquid State Machine computation model; (2) a novel learning algorithm; and (3) an intelligently designed unsupervised readout based on Fuzzy c-Means clustering of spike responses from a subset of neurons (Liquid states), selected using particle swarm optimization.