Gianluca Zoppo

Orcid: 0000-0003-0765-4333

Affiliations:
  • Polytechnic University of Turin, Italy


According to our database1, Gianluca Zoppo authored at least 13 papers between 2019 and 2023.

Collaborative distances:
  • Dijkstra number2 of four.
  • Erdős number3 of four.

Timeline

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Bibliography

2023
Spectral Ranking in Complex Networks Using Memristor Crossbars.
IEEE J. Emerg. Sel. Topics Circuits Syst., March, 2023

Gaussian Process for Nonlinear Regression via Memristive Crossbars.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2023

Memristor-based Offset Cancellation Technique in Analog Crossbars.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2023

2022
A Dynamic System Approach to Spiking Second Order Memristor Networks.
IEEE Trans. Circuits Syst. I Regul. Pap., 2022

A Mathematical Analysis of Wire Resistance Problem in Memristor Crossbars.
Proceedings of the 19th International SoC Design Conference, 2022

Equilibrium Propagation and (Memristor-based) Oscillatory Neural Networks.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2022

Analog Acceleration of the Power Method using Memristor Crossbars.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2022

2021
Analog Solutions of Discrete Markov Chains via Memristor Crossbars.
IEEE Trans. Circuits Syst. I Regul. Pap., 2021

A Dynamic System Approach to Spiking Memristor Network Investigation.
Proceedings of the 64th IEEE International Midwest Symposium on Circuits and Systems, 2021

Local Learning in Memristive Neural Networks for Pattern Reconstruction.
Proceedings of the IEEE International Symposium on Circuits and Systems, 2021

2020
Pattern Characterization in Second Order Memristor Networks.
Proceedings of the 63rd IEEE International Midwest Symposium on Circuits and Systems, 2020

2019
Second Order Memristor Models for Neuromorphic Computing.
Proceedings of the 62nd IEEE International Midwest Symposium on Circuits and Systems, 2019

A Continuous-time Learning Rule for Memristor-based Recurrent Neural Networks.
Proceedings of the 26th IEEE International Conference on Electronics, Circuits and Systems, 2019


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