Publications

@conference{JLF2023b,

title = {Oikonomos-II: A Reinforcement-Learning, Resource-Recommendation System for Cloud HPC},

author = {Betting JLF and De Zeeuw CI and Strydis C },

url = {https://neurocomputinglab.com/wp-content/uploads/2024/02/Oikonomos_2_Final.pdf, Download Paper},

year  = {2023},

date = {2023-12-18},

urldate = {2023-12-18},

booktitle = {IEEE 30th International Conference on High Performance Computing, Data, and Analytics (HiPC)},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {conference}

}

@conference{JLF2023,

title = {Oikonomos: An Opportunistic, Deep-Learning, Resource-Recommendation System for Cloud HPC},

author = {Betting JLF and Liakopoulos D and Engelen M and Strydis C},

url = {https://neurocomputinglab.com/wp-content/uploads/2024/02/Oikonomos_1_final-3.pdf, Download Paper},

doi = {10.1109/ASAP57973.2023.00039},

year  = {2023},

date = {2023-07-19},

urldate = {2023-07-19},

booktitle = {2023 IEEE 34th International Conference on Application-specific Systems, Architectures and Processors (ASAP)},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {conference}

}

@conference{C2021,

title = {PlasticNet+: Extending Multi-FPGA Interconnect Architecture via Gigabit Transceivers},

author = {Salazar-García C and García-Ramírez R and Rímolo-Donadío R and Strydis C and Chacón-Rodríguez A},

url = {https://neurocomputinglab.com/wp-content/uploads/2022/09/PlasticNet_Extending_Multi-FPGA_Interconnect_Architecture_via_Gigabit_Transceivers.pdf, Download Paper},

doi = {10.1109/ISCAS51556.2021.9401058},

year  = {2021},

date = {2021-04-27},

booktitle = {International Symposium on Circuits and Systems (ISCAS)},

publisher = {IEEE},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {conference}

}

@conference{S2020b,

title = { A novel simulator for extended Hodgkin-Huxley neural network},

author = {Panagiotou S and Miedema R and Sidiropoulos H and Smaragdos G and Strydis C and Soudris D},

url = {https://neurocomputinglab.com/wp-content/uploads/2022/05/BIBE2020accepted.pdf, Download Paper},

doi = {10.1109/BIBE50027.2020.00071},

year  = {2020},

date = {2020-10-26},

booktitle = {BIBE2020},

publisher = {IEEE},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {conference}

}

@conference{R2020b,

title = {Pre-Synthesis Evaluation of Digital Bus Micro-Architectures},

author = { Garcia-Ramirez R and Chacón-Rodríguez A and Strydis C and Rimolo-Donadio R},

url = {https://neurocomputinglab.com/wp-content/uploads/2020/12/garcia-ramirez2020.pdf, Download Paper},

doi = {10.1109/PRIME-LA47693.2020.9062719},

year  = {2020},

date = {2020-02-25},

booktitle = {Conference on PhD Research in Microelectronics and Electronics in Latin America (PRIME-LA)},

publisher = {IEEE},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {conference}

}

@conference{Miedema2020,

title = {Synthesis-Free, Flexible and Fast Hardware Library for Biophysically Plausible Neurosimulations - POSTER},

author = { Miedema R and Smaragdos G and Negrello M and Al-Ars Z and Möller M and Strydis C },

url = {https://neurocomputinglab.com/flexhh_poster-2/, Download Poster},

doi = {10.1145/3373087.3375374},

year  = {2020},

date = {2020-02-20},

booktitle = { International Symposium on Field-Programmable Gate Arrays },

organization = {ACM/SIGDA},

abstract = {Computational neuroscience uses models to study the brain. The Hodgkin-Huxley (HH) model, and its extensions, is one of the most powerful, biophysically meaningful models currently used. The high experimental value of the (extended) Hodgkin-Huxley (eHH) models comes at the cost of steep computational requirements. Consequently, for larger networks, neuroscientists either opt for simpler models, losing neuro-computational features, or use high-performance computing systems. The eHH models can be efficiently implemented as a dataflow application on a FPGA-based architecture. The state-of-the-art FPGA-based implementations have proven to be time-consuming because of the long-duration synthesis requirements. We have developed flexHH, a flexible hardware library, compatible with a widely used neuron-model description format, implementing five FPGA-accelerated and parameterizable variants of eHH models (standard HH with optional extensions: custom ion-gates, gap junctions, and/or multiple cell compartments). Therefore, flexHH is a crucial step towards high-flexibility and high-performance FPGA-based simulations, eschewing the penalty of re-engineering and re-synthesis, dismissing the need for an engineer. In terms of performance, flexHH achieves a speedup of 1,065x against NEURON, the simulator standard in computational neuroscience, and speedups between 8x-20x against sequential C. Furthermore, flexHH is faster per simulation step compared to other HPC technologies, provides 65% or better performance density (in FLOPS/LUT) compared to related works, and only shows a marginal performance drop in real-time simulations.},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {conference}

}

@conference{Neofytou2020,

title = {GPU Implementation of Neural-Network Simulations based on Adaptive-Exponential Models},

author = {Neofytou A and Chatzikonstantis G and Magkanaris I and Smaragdos G and Strydis C and Soudris D },

url = {https://neurocomputinglab.com/wp-content/uploads/2020/06/BIBE2019Neofytou-2.pdf, Download paper},

doi = {10.1109/BIBE.2019.00067},

year  = {2019},

date = {2019-10-28},

booktitle = {19th International Conference on Bioinformatics and Bioengineering (BIBE) },

organization = {IEEE},

abstract = {Detailed brain modeling has been presenting significant challenges to the world of high-performance computing (HPC), posing computational problems that can benefit from modern hardware-acceleration technologies. We explore the capacity of GPUs for simulating large-scale neuronal networks based on the Adaptive Exponential neuron-model, which is widely used in the neuroscientific community. Our GPU-powered simulator acts as a benchmark to evaluate the strengths and limitations of modern GPUs, as well as to explore their scaling properties when simulating large neural networks. This work presents an optimized GPU implementation that outperforms a reference multicore implementation by 50x, whereas utilizing a dual-GPU configuration can deliver a speedup of 90x for networks of 20,000 fully interconnected AdEx neurons.},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {conference}

}

@conference{Alfaro-Badilla2020,

title = {Improving the Simulation of Biologically Accurate Neural Networks Using Data Flow HLS Transformations on Heterogeneous SoC-FPGA Platforms},

author = {Alfaro-Badilla K and Arroyo-Romero A and Salazar-García C and Vega LGL and Espinoza-González J and Hernández- Castro F and Chacón-Rodríguez A and Smaragdos G and Strydis C },

doi = {10.1007/978-3-030-41005-6_13},

year  = {2019},

date = {2019-09-25},

booktitle = {CARLA 2019},

publisher = {Springer},

abstract = {This work proposes a hardware performance-oriented design methodology aimed at generating efficient high-level synthesis (HLS) coded data multiprocessing on a heterogeneous platform. The methodology is tested on typical neuroscientific complex application: the biologically accurate modeling of a brain region known as the inferior olivary nucleus (ION). The ION cells are described using a multi-compartmental model based on the extended Hodgkin-Huxley membrane model (eHH), which requires the solution of a set of coupled differential equations. The proposed methodology is tested against alternative HPC implementations (multi-core CPU i7-7820HQ, and a Virtex7 FPGA) of the same ION model for different neural network sizes. Results show that the solution runs 10 to 4 times faster than our previous implementation using the same board and closes the gap between the performance against a Virtex7 implementation without using at full-capacity the AXI-HP channels.},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {conference}

}

@conference{Alfaro-Badilla2019,

title = {Prototyping a Biologically Plausible Neuron Model on a Heterogeneous CPU-FPGA Board},

author = {Alfaro-Badilla K and Chacón-Rodríguez A and Smaragdos G and Strydis C and Arroyo-Romero A and Espinoza-González J and Salazar-García C},

url = {https://ieeexplore.ieee.org/abstract/document/8667538},

year  = {2019},

date = {2019-02-24},

publisher = {10th Latin American Symposium on Circuits & Systems (LASCAS)},

organization = {IEEE},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {conference}

}

@conference{H2018,

title = {The VINEYARD Framework for Heterogeneous Cloud Applications: The BrainFrame Case},

author = {Sidiropoulos H and Chatzikonstantis G and Soudris D and Strydis C },

url = {https://neurocomputinglab.com/wp-content/uploads/2020/12/sidiropoulos2018.pdf, Download Paper},

doi = {10.1109/DASIP.2018.8597119},

year  = {2018},

date = {2018-10-10},

booktitle = {DASIP 2018},

pages = {70-75},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {conference}

}

@conference{Chatzikonstantis2017,

title = {From Knights Corner to Landing: a Case Study Based on a Hodgkin-Huxley Neuron Simulator},

author = {Chatzikonstantis G and Jiménez D and Meneses E and Strydis C and Sidiropoulos H and Soudris D},

url = {https://neurocomputinglab.com/wp-content/uploads/2020/06/From-Knights-Corner-to-Landing.pdf, Download Paper},

year  = {2017},

date = {2017-06-18},

booktitle = {International Conference on High Performance Computing},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {conference}

}

@conference{D2016,

title = {mCluster: A Software Framework for Portable Device-Based Volunteer Computing},

author = {Theodoropoulos D and Chrysos G and Koidis I and Charitopoulos G and Pissadakis E and Varikos A and Pnevmatikatos D and Smaragdos G and Strydis C and Zervos N},

url = {https://neurocomputinglab.com/wp-content/uploads/2020/06/ccgrid2016.pdf, Download Paper},

year  = {2016},

date = {2016-07-21},

booktitle = {16th International Symposium on Cluster, Cloud and Grid Computing (CCGrid 2016)},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {conference}

}

@conference{C2016,

title = {The VINEYARD project: Versatile integrated accelerator-based heterogeneous data centres},

author = {Kachris C and Soudris D and Gaydadjiev G and Nguyen H and Nikolopoulos DS and Bilas A and Morgan N and Strydis C and Tsalidis C and Balafas J and Jimenez-Peris R and Almeida A},

url = {https://neurocomputinglab.com/wp-content/uploads/2020/06/kachris201_170207163348.pdf, Download Paper},

year  = {2016},

date = {2016-06-23},

publisher = {MOCAST 2016},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {conference}

}

@conference{G2016b,

title = {Performance Analysis of Accelerated Biophysically-Meaningful Neuron Simulations},

author = {Smaragdos G and Chatzikonstantis G and Nomikou S and Rodopoulos D and Sourdis I and Soudris D and De Zeeuw CI and Strydis C},

url = {https://neurocomputinglab.com/wp-content/uploads/2020/06/ispass2016.pdf, Download Paper},

year  = {2016},

date = {2016-06-02},

booktitle = {International Symposium on Performance Analysis of Systems and Software},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {conference}

}

@conference{G2016,

title = {First Impressions from Detailed Brain Model Simulations on a Xeon-Xeon Phi Node},

author = {Chatzikonstantis G and Rodopoulos D and Nomikou S and Strydis C and De Zeeuw CI and Soudris D},

url = {https://neurocomputinglab.com/wp-content/uploads/2020/06/2016-cfpd_160509155010.pdf, Download Paper},

year  = {2016},

date = {2016-05-16},

booktitle = {ACM International Conference on Computing Frontiers 2016},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {conference}

}

@conference{A2016,

title = {Runtime Interval Optimization and Dependable Performance for Application-Level Checkpointing},

author = {Kokolis A and Mavrogiannis A and Rodopoulos D and Strydis C and Soudris D},

url = {https://dl.acm.org/doi/10.5555/2971808.2971947, Download Paper},

year  = {2016},

date = {2016-03-14},

publisher = {DATE 2016},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {conference}

}

@conference{C2016b,

title = {The VINEYARD approach: Versatile, Integrated, Accelerator-based, Heterogeneous Data Centres},

author = {Kachris C and Soudris D and Gaydadjiev G and Nguyen H and Nikolopoulos DS and Bilas A and Morgan N and Strydis C and Tsalidis C and Balafas J and Jimenez-Peris R and Almeida A},

url = {https://neurocomputinglab.com/wp-content/uploads/2020/06/2016ARC.pdf, Download Paper},

year  = {2016},

date = {2016-03-13},

publisher = {ARC 2016},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {conference}

}

@conference{Z2015,

title = {Accelerating complex brain-model simulations on GPU platforms},

author = {Nguyen HAD and Al-Ars Z and Smaragdos G and Strydis C},

url = {http://ce-publications.et.tudelft.nl/publication/view/id/1475},

year  = {2015},

date = {2015-03-23},

booktitle = {Design, Automation, and Test in Europe, DATE 2015},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {conference}

}

@conference{vanM2014,

title = {ESL Design of Customizable Real-Time Neuron Networks},

author = {van Eijk M and Galuzzi C and Zjajo A and Smaragdos G and Strydis C and van Leuken R},

url = {https://neurocomputinglab.com/wp-content/uploads/2020/06/Biocas_Paper_2014_galuzzi_camera_ready.pdf, Download Paper},

doi = {10.1109/BioCAS.2014.6981815 },

year  = {2014},

date = {2014-12-11},

booktitle = {IEEE Biomedical Circuits and Systems Conference (BIOCAS 2014)},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {conference}

}

@conference{D2014,

title = {Optimal Mapping of Inferior Olive Neuron Simulations on the Single-Chip Cloud Computer},

author = {Rodopoulos D and Chatzikonstantis G and Pantelopoulos A and Soudris D and De Zeeuw CI and Strydis C},

url = {https://neurocomputinglab.com/wp-content/uploads/2020/06/RodopoulosSAMOS20142.pdf, Download Paper},

doi = {10.1109/SAMOS.2014.6893235},

year  = {2014},

date = {2014-09-08},

booktitle = {International Conference on Embedded Computer Systems: Architectures, Modeling, and Simulation (SAMOS XIV)},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {conference}

}

@conference{G2014b,

title = {Real-Time Olivary Neuron Simulations on Dataflow Computing Machines},

author = {Smaragdos G and Davies C and Strydis C and Sourdis I and Ciobanu C and Mencer O and De Zeeuw CI},

url = {https://www.researchgate.net/publication/271522055_Real-Time_Olivary_Neuron_Simulations_on_Dataflow_Computing_Machines, Download Paper},

doi = {10.1007/978-3-319-07518-1_34},

year  = {2014},

date = {2014-06-22},

booktitle = {International Supercomputing Conference (ISC)},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {conference}

}

@conference{I2014,

title = {DeSyRe: On-Demand Adaptive and Reconfigurable Fault-Tolerant SoCs},

author = {Sourdis I and Strydis C and Armato A and Bouganis CS and Falsafi B and Gaydadjiev GN and Isaza S and Malek A and Mariani R and Pradhan DK and Rauwerda G and Seepers RM and Shafik RA and Smaragdos G and Theodoropoulos D and Tzilis S and Vavouras M and Pagliarini S and Pnevmatikatos D},

url = {http://erasmusbrainproject.com/media/PDFresources/PublicationsSINS/ARC2014.pdf},

year  = {2014},

date = {2014-04-14},

booktitle = {10th International Symposium on Applied Reconfigurable Computing (ARC)},

keywords = {BrainFrame, SIMS},

pubstate = {published},

tppubtype = {conference}

}

@conference{G2014c,

title = {FPGA-based Biophysically-Meaningful Modeling of Olivocerebellar Neurons},

author = {Smaragdos G and Isaza S and Van Eijk M and Sourdis I and Strydis C},

url = {https://neurocomputinglab.com/wp-content/uploads/2020/12/2554688.2554790-3.pdf, Download Paper},

doi = {10.1145/2554688.2554790},

year  = {2014},

date = {2014-02-26},

booktitle = {22nd ACM/SIGDA International Symposium on Field-Programmable Gate Arrays (FPGA)},

keywords = {BrainFrame},

pubstate = {published},

tppubtype = {conference}

}

@conference{I2012,

title = {The DeSyRe project: on-Demand System Reliability},

author = {Sourdis I and Strydis C and Bouganis CS and Falsafi B and Gaydadjiev GN and Malek A and Mariani R and Pnevmatikatos DN and Pradhan DK and Rauwerda G and Sunesen K and Tzilis S},

doi = {10.1016/j.micpro.2013.08.008},

year  = {2012},

date = {2012-09-05},

booktitle = {15th EUROMICRO Conference on Digital System Design (DSD)},

keywords = {BrainFrame, SIMS},

pubstate = {published},

tppubtype = {conference}

}