89 citations · 180 across the 30 of their papers we have counts for
31 papers
Don't let your Memory defy you: Fragmentation-Aware Serverless Allocation with Elastic Memory Locality
Achilleas Tzenetopoulos, Dimosthenis Masouros, Sotirios Xydis +2
Serverless platforms commonly rely on bundled, memory-centric configurations, where CPU capacity follows the specified memory size. Resource decoupling reduces this waste, but can…
Deep Learning for Joint Narrowband Interference Cancellation and Soft Demodulation in OFDM Systems
Emmanouil Kavvousanos, Francky Catthoor, Vassilis Paliouras
Narrowband interference (NBI) severely degrades orthogonal frequency-division multiplexing (OFDM) systems by corrupting subcarriers and rendering classical soft demodulation ineffe…
Optimizing GEMM for Energy and Performance on Versal ACAP Architectures
Ilias Papalamprou, Dimosthenis Masouros, Ioannis Loudaros +2
General Matrix Multiplication (GEMM) is a fundamental operation in many scientific workloads, signal processing, and particularly deep learning. It is often a bottleneck for perfor…
The Unexpected Efficiency of Bin Packing Algorithms for Dynamic Storage Allocation in the Wild: An Intellectual Abstract
Christos P. Lamprakos, Sotirios Xydis, Francky Catthoor +1
Recent work has shown that viewing allocators as black-box 2DBP solvers bears meaning. For instance, there exists a 2DBP-based fragmentation metric which often correlates monotonic…
Viewing Allocators as Bin Packing Solvers Demystifies Fragmentation
Christos P. Lamprakos, Sotirios Xydis, Francky Catthoor +1
This paper presents a trace-based simulation methodology for constructing representations of workload-allocator interaction. We use two-dimensional rectangular bin packing (2DBP) a…
Energy Consumption Evaluation of Optane DC Persistent Memory for Indexing Data Structures
Manolis Katsaragakis, Christos Baloukas, Lazaros Papadopoulos +3
The Intel Optane DC Persistent Memory (DCPM) is an attractive novel technology for building storage systems for data intensive HPC applications, as it provides lower cost per byte,…
Timescales: the choreography of classical and unconventional computing
Herbert Jaeger, Francky Catthoor
Tasks that one wishes to have done by a computer often come with conditions that relate to timescales. For instance, the processing must terminate within a given time limit; or a s…
A Soft SIMD Based Energy Efficient Computing Microarchitecture
Pengbo Yu, Alexandre Levisse, Mohit Gupta +4
The ever-increasing size and computational complexity of today's machine-learning algorithms pose an increasing strain on the underlying hardware. In this light, novel and dedicate…
VWR2A: A Very-Wide-Register Reconfigurable-Array Architecture for Low-Power Embedded Devices
Benoît Walter Denkinger, Miguel Peón-Quirós, Mario Konijnenburg +2
Edge-computing requires high-performance energy-efficient embedded systems. Fixed-function or custom accelerators, such as FFT or FIR filter engines, are very efficient at implemen…
Continuously Learning to Detect People on the Fly: A Bio-inspired Visual System for Drones
Ali Safa, Ilja Ocket, André Bourdoux +3
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…
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…
Enabling Resource-Aware Mapping of Spiking Neural Networks via Spatial Decomposition
Adarsha Balaji, Shihao Song, Anup Das +5
With growing model complexity, mapping Spiking Neural Network (SNN)-based applications to tile-based neuromorphic hardware is becoming increasingly challenging. This is because the…
Run-time Mapping of Spiking Neural Networks to Neuromorphic Hardware
Adarsha Balaji, Thibaut Marty, Anup Das +1
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}.…
PyCARL: A PyNN Interface for Hardware-Software Co-Simulation of Spiking Neural Network
Adarsha Balaji, Prathyusha Adiraju, Hirak J. Kashyap +4
We present PyCARL, a PyNN-based common Python programming interface for hardware-software co-simulation of spiking neural network (SNN). Through PyCARL, we make the following two k…
A Framework to Explore Workload-Specific Performance and Lifetime Trade-offs in Neuromorphic Computing
Adarsha Balaji, Shihao Song, Anup Das +4
Neuromorphic hardware with non-volatile memory (NVM) can implement machine learning workload in an energy-efficient manner. Unfortunately, certain NVMs such as phase change memory…
Mapping Spiking Neural Networks to Neuromorphic Hardware
Adarsha Balaji, Anup Das, Yuefeng Wu +7
Neuromorphic hardware platforms implement biological neurons and synapses to execute spiking neural networks (SNNs) in an energy-efficient manner. We present SpiNeMap, a design met…