Erik S. Skibinsky-Gitlin

According to our database, Erik S. Skibinsky-Gitlin authored at least 11 papers between 2018 and 2024.

Collaborative distances:

• Dijkstra number of five.
• Erdős number of four.

2024

Deep Learning Inference on Edge: A Preliminary Device Comparison.

Manuel Luis González Hernandez, Jorge Ruiz, Lidia Andrés, Randy Lozada, Erik S. Skibinsky-Gitlin, Jorge Fernández, Javier Sedano, Ángel Miguel García-Vico

Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2024, 2024

2023

Fully Parallel Stochastic Computing Hardware Implementation of Convolutional Neural Networks for Edge Computing Applications.

Christiam F. Frasser, Pablo Linares-Serrano, Iván Díez de los Ríos, Alejandro Morán, Erik S. Skibinsky-Gitlin, Joan Font-Rosselló, Vincent Canals, Miquel Roca, Teresa Serrano-Gotarredona, Josep L. Rosselló

IEEE Trans. Neural Networks Learn. Syst., December, 2023

Highly Optimized Hardware Morphological Neural Network Through Stochastic Computing and Tropical Pruning.

Josep L. Rosselló, Joan Font-Rosselló, Christiam F. Frasser, Alejandro Morán, Erik S. Skibinsky-Gitlin, Vincent Canals, Miquel Roca

IEEE J. Emerg. Sel. Topics Circuits Syst., March, 2023

FAS-CT: FPGA-Based Acceleration System with Continuous Training.

Manuel Luis González Hernandez, Jorge Ruiz, Randy Lozada, Erik Sebastian Skibinsky Gitlin, Ángel Miguel García-Vico, Javier Sedano, José Ramón Villar

Proceedings of the Communication Papers of the 18th Conference on Computer Science and Intelligence Systems, 2023

2022

Hardware Implementation of Stochastic Computing-based Morphological Neural Systems.

Josep L. Rosselló, Joan Font-Rosselló, Christiam F. Frasser, Alejandro Morán, Erik S. Skibinsky-Gitlin, Vicent Canals, Miquel Roca

Proceedings of the IEEE International Symposium on Circuits and Systems, 2022

Performance/Resources Comparison of Hardware Implementations on Fully Connected Network Inference.

Randy Lozada, Jorge Ruiz, Manuel Luis González Hernandez, Javier Sedano, José R. Villar, Ángel Miguel García-Vico, Erik S. Skibinsky-Gitlin

Proceedings of the Intelligent Data Engineering and Automated Learning - IDEAL 2022, 2022

Improving efficiency of a Stochastic Computing-based Morphological Neural Network.

Erik S. Skibinsky-Gitlin, Joan Font-Rosselló, Christiam F. Frasser, Alejandro Morán, Vicens Canals, Miquel Roca, Josep L. Rosselló

Proceedings of the 37th Conference on Design of Circuits and Integrated Systems, 2022

2021

Stochastic Computing co-processing elements for Evolving Autonomous Data Partitioning.

Alejandro Morán, Vincent Canals, Plamen P. Angelov, Christiam F. Frasser, Erik S. Skibinsky-Gitlin, Joan Font, Eugeni Isern, Miquel Roca, Josep L. Rosselló

Proceedings of the XXXVI Conference on Design of Circuits and Integrated Systems, 2021

2020

Efficient parallel implementation of reservoir computing systems.

Miquel L. Alomar, Erik S. Skibinsky-Gitlin, Christiam F. Frasser, Vincent Canals, Eugeni Isern, Miquel Roca, Josep L. Rosselló

Neural Comput. Appl., 2020

2018

Reservoir Computing Hardware for Time Series Forecasting.

Erik S. Skibinsky-Gitlin, Miquel L. Alomar, Eugeni Isern, Miquel Roca, Vincent Canals, Josep L. Rosselló

Proceedings of the 28th International Symposium on Power and Timing Modeling, 2018

Cyclic Reservoir Computing with FPGA Devices for Efficient Channel Equalization.

Erik S. Skibinsky-Gitlin, Miquel L. Alomar, Christiam F. Frasser, Vincent Canals, Eugeni Isern, Miquel Roca, Josep L. Rosselló

Proceedings of the Artificial Intelligence and Soft Computing, 2018