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Erik S. Skibinsky-Gitlin
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2020 – today
- 2024
- [c8]Manuel Luis González Hernandez, Jorge Ruiz, Lidia Andrés, Randy Lozada, Erik S. Skibinsky-Gitlin, Jorge Fernández, Javier Sedano, Ángel Miguel García-Vico:
Deep Learning Inference on Edge: A Preliminary Device Comparison. IDEAL (1) 2024: 265-276 - 2023
- [j3]Josep L. Rosselló
, Joan Font-Rosselló
Highly Optimized Hardware Morphological Neural Network Through Stochastic Computing and Tropical Pruning. IEEE J. Emerg. Sel. Topics Circuits Syst. 13(1): 249-256 (2023) - [j2]Christiam F. Frasser
Fully Parallel Stochastic Computing Hardware Implementation of Convolutional Neural Networks for Edge Computing Applications. IEEE Trans. Neural Networks Learn. Syst. 34(12): 10408-10418 (2023) - [c7]Manuel Luis González Hernandez, Jorge Ruiz, Randy Lozada
FAS-CT: FPGA-Based Acceleration System with Continuous Training. FedCSIS (Communication Papers) 2023: 131-139 - 2022
- [c6]Erik S. Skibinsky-Gitlin, Joan Font-Rosselló
Improving efficiency of a Stochastic Computing-based Morphological Neural Network. DCIS 2022: 1-6 - [c5]Randy Lozada, Jorge Ruiz
Performance/Resources Comparison of Hardware Implementations on Fully Connected Network Inference. IDEAL 2022: 348-358 - [c4]Josep L. Rosselló, Joan Font-Rosselló
Hardware Implementation of Stochastic Computing-based Morphological Neural Systems. ISCAS 2022: 356-360 - 2021
- [c3]Alejandro Morán
Stochastic Computing co-processing elements for Evolving Autonomous Data Partitioning. DCIS 2021: 1-6 - 2020
- [j1]Miquel L. Alomar, Erik S. Skibinsky-Gitlin
Efficient parallel implementation of reservoir computing systems. Neural Comput. Appl. 32(7): 2299-2313 (2020)
2010 – 2019
- 2018
- [c2]Erik S. Skibinsky-Gitlin, Miquel L. Alomar, Christiam F. Frasser, Vincent Canals, Eugeni Isern
Cyclic Reservoir Computing with FPGA Devices for Efficient Channel Equalization. ICAISC (1) 2018: 226-234 - [c1]Erik S. Skibinsky-Gitlin
Reservoir Computing Hardware for Time Series Forecasting. PATMOS 2018: 133-139
Coauthor Index
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