89 citations · 180 across the 30 of their papers we have counts for
9 papers · 1 filter
MemPool-3D: Boosting Performance and Efficiency of Shared-L1 Memory Many-Core Clusters with 3D Integration
Matheus Cavalcante, Anthony Agnesina, Samuel Riedel +6
Three-dimensional integrated circuits promise power, performance, and footprint gains compared to their 2D counterparts, thanks to drastic reductions in the interconnects' length t…
Design of Many-Core Big Little μBrain for Energy-Efficient Embedded Neuromorphic Computing
M. Lakshmi Varshika, Adarsha Balaji, Federico Corradi +3
As spiking-based deep learning inference applications are increasing in embedded systems, these systems tend to integrate neuromorphic accelerators such as Brain to improve ener…
A New Look at Spike-Timing-Dependent Plasticity Networks for Spatio-Temporal Feature Learning
Ali Safa, Ilja Ocket, André Bourdoux +3
We present new theoretical foundations for unsupervised Spike-Timing-Dependent Plasticity (STDP) learning in spiking neural networks (SNNs). In contrast to empirical parameter sear…
Fail-Safe Human Detection for Drones Using a Multi-Modal Curriculum Learning Approach
Ali Safa, Tim Verbelen, Ilja Ocket +3
Drones are currently being explored for safety-critical applications where human agents are expected to evolve in their vicinity. In such applications, robust people avoidance must…
A 2-J, 12-class, 91% Accuracy Spiking Neural Network Approach For Radar Gesture Recognition
Ali Safa, André Bourdoux, Ilja Ocket +2
Radar processing via spiking neural networks (SNNs) has recently emerged as a solution in the field of ultra-low-power wireless human-computer interaction. Compared to traditional…
A Low-Complexity Radar Detector Outperforming OS-CFAR for Indoor Drone Obstacle Avoidance
Ali Safa, Tim Verbelen, Lars Keuninckx +5
As radar sensors are being miniaturized, there is a growing interest for using them in indoor sensing applications such as indoor drone obstacle avoidance. In those novel scenarios…
Dynamic Reliability Management in Neuromorphic Computing
Shihao Song, Jui Hanamshet, Adarsha Balaji +5
Neuromorphic computing systems uses non-volatile memory (NVM) to implement high-density and low-energy synaptic storage. Elevated voltages and currents needed to operate NVMs cause…
NeuroXplorer 1.0: An Extensible Framework for Architectural Exploration with Spiking Neural Networks
Adarsha Balaji, Shihao Song, Twisha Titirsha +6
Recently, both industry and academia have proposed many different neuromorphic architectures to execute applications that are designed with Spiking Neural Network (SNN). Consequent…
Endurance-Aware Mapping of Spiking Neural Networks to Neuromorphic Hardware
Twisha Titirsha, Shihao Song, Anup Das +4
Neuromorphic computing systems are embracing memristors to implement high density and low power synaptic storage as crossbar arrays in hardware. These systems are energy efficient…