Alberto Marchisio
Orcid: 0000-0002-0689-4776
According to our database1,
Alberto Marchisio
authored at least 56 papers
between 2018 and 2024.
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Bibliography
2024
Cutting is All You Need: Execution of Large-Scale Quantum Neural Networks on Limited-Qubit Devices.
CoRR, 2024
CoRR, 2024
SNN4Agents: A Framework for Developing Energy-Efficient Embodied Spiking Neural Networks for Autonomous Agents.
CoRR, 2024
TinyCL: An Efficient Hardware Architecture for Continual Learning on Autonomous Systems.
CoRR, 2024
AdvQuNN: A Methodology for Analyzing the Adversarial Robustness of Quanvolutional Neural Networks.
Proceedings of the IEEE International Conference on Quantum Software, 2024
Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2024
Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2024
Proceedings of the IEEE International Conference on Quantum Computing and Engineering, 2024
Proceedings of the International Joint Conference on Neural Networks, 2024
Embodied Neuromorphic Artificial Intelligence for Robotics: Perspectives, Challenges, and Research Development Stack.
Proceedings of the 17th International Conference on Control, 2024
A Methodology to Study the Impact of Spiking Neural Network Parameters Considering Event-Based Automotive Data.
Proceedings of the 17th International Conference on Control, 2024
FastSpiker: Enabling Fast Training for Spiking Neural Networks on Event-based Data Through Learning Rate Enhancements for Autonomous Embedded Systems.
Proceedings of the 17th International Conference on Control, 2024
2023
ISMatch: A real-time hardware accelerator for inexact string matching of DNA sequences on FPGA.
Microprocess. Microsystems, March, 2023
SeVuc: A study on the Security Vulnerabilities of Capsule Networks against adversarial attacks.
Microprocess. Microsystems, February, 2023
Inf., 2023
A Survey on Quantum Machine Learning: Current Trends, Challenges, Opportunities, and the Road Ahead.
CoRR, 2023
RobCaps: Evaluating the Robustness of Capsule Networks against Affine Transformations and Adversarial Attacks.
Proceedings of the International Joint Conference on Neural Networks, 2023
Proceedings of the International Joint Conference on Neural Networks, 2023
2022
AccelAT: A Framework for Accelerating the Adversarial Training of Deep Neural Networks Through Accuracy Gradient.
IEEE Access, 2022
RoHNAS: A Neural Architecture Search Framework With Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks.
IEEE Access, 2022
Special Session: Towards an Agile Design Methodology for Efficient, Reliable, and Secure ML Systems.
Proceedings of the 40th IEEE VLSI Test Symposium, 2022
Enabling Capsule Networks at the Edge through Approximate Softmax and Squash Operations.
Proceedings of the ISLPED '22: ACM/IEEE International Symposium on Low Power Electronics and Design, Boston, MA, USA, August 1, 2022
LaneSNNs: Spiking Neural Networks for Lane Detection on the Loihi Neuromorphic Processor.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2022
fakeWeather: Adversarial Attacks for Deep Neural Networks Emulating Weather Conditions on the Camera Lens of Autonomous Systems.
Proceedings of the International Joint Conference on Neural Networks, 2022
CoNLoCNN: Exploiting Correlation and Non-Uniform Quantization for Energy-Efficient Low-precision Deep Convolutional Neural Networks.
Proceedings of the International Joint Conference on Neural Networks, 2022
2021
FEECA: Design Space Exploration for Low-Latency and Energy-Efficient Capsule Network Accelerators.
IEEE Trans. Very Large Scale Integr. Syst., 2021
IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2021
R-SNN: An Analysis and Design Methodology for Robustifying Spiking Neural Networks against Adversarial Attacks through Noise Filters for Dynamic Vision Sensors.
Proceedings of the IEEE/RSJ International Conference on Intelligent Robots and Systems, 2021
CarSNN: An Efficient Spiking Neural Network for Event-Based Autonomous Cars on the Loihi Neuromorphic Research Processor.
Proceedings of the International Joint Conference on Neural Networks, 2021
DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural Networks.
Proceedings of the International Joint Conference on Neural Networks, 2021
Towards Energy-Efficient and Secure Edge AI: A Cross-Layer Framework ICCAD Special Session Paper.
Proceedings of the IEEE/ACM International Conference On Computer Aided Design, 2021
Securing Deep Spiking Neural Networks against Adversarial Attacks through Inherent Structural Parameters.
Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2021
MLComp: A Methodology for Machine Learning-based Performance Estimation and Adaptive Selection of Pareto-Optimal Compiler Optimization Sequences.
Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2021
2020
An Updated Survey of Efficient Hardware Architectures for Accelerating Deep Convolutional Neural Networks.
Future Internet, 2020
Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead.
IEEE Access, 2020
NeuroAttack: Undermining Spiking Neural Networks Security through Externally Triggered Bit-Flips.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020
An Efficient Spiking Neural Network for Recognizing Gestures with a DVS Camera on the Loihi Neuromorphic Processor.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020
Is Spiking Secure? A Comparative Study on the Security Vulnerabilities of Spiking and Deep Neural Networks.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020
FasTrCaps: An Integrated Framework for Fast yet Accurate Training of Capsule Networks.
Proceedings of the 2020 International Joint Conference on Neural Networks, 2020
NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networks.
Proceedings of the IEEE/ACM International Conference On Computer Aided Design, 2020
ReD-CaNe: A Systematic Methodology for Resilience Analysis and Design of Capsule Networks under Approximations.
Proceedings of the 2020 Design, Automation & Test in Europe Conference & Exhibition, 2020
Proceedings of the 57th ACM/IEEE Design Automation Conference, 2020
A Fast Design Space Exploration Framework for the Deep Learning Accelerators: Work-in-Progress.
Proceedings of the International Conference on Hardware/Software Codesign and System Synthesis, 2020
2019
X-TrainCaps: Accelerated Training of Capsule Nets through Lightweight Software Optimizations.
CoRR, 2019
CapStore: Energy-Efficient Design and Management of the On-Chip Memory for CapsuleNet Inference Accelerators.
CoRR, 2019
SNN under Attack: are Spiking Deep Belief Networks vulnerable to Adversarial Examples?
CoRR, 2019
CoRR, 2019
Deep Learning for Edge Computing: Current Trends, Cross-Layer Optimizations, and Open Research Challenges.
Proceedings of the 2019 IEEE Computer Society Annual Symposium on VLSI, 2019
Proceedings of the Design, Automation & Test in Europe Conference & Exhibition, 2019
2018
X-DNNs: Systematic Cross-Layer Approximations for Energy-Efficient Deep Neural Networks.
J. Low Power Electron., 2018
A Methodology for Automatic Selection of Activation Functions to Design Hybrid Deep Neural Networks.
CoRR, 2018
Proceedings of the 2018 International Joint Conference on Neural Networks, 2018
Proceedings of the Workshop on INTelligent Embedded Systems Architectures and Applications, 2018