Alberto Marchisio

Alberto Marchisio
University Assistant Projektass. Dott.mag. BSc
profile.png
E-Mail
Phone +43 (1) 58801 - 18203
Fax +43 (1) 58801 - 18297
Address



Vienna University of Technology
Institute of Computer Engineering
Embedded Computing Systems
Treitlstrasse 3, 2nd floor, 1040 Wien, Austria

Short Biography

Mr. Alberto Marchisio received his B.Sc. degree in Electronic Engineering from Politecnico di Torino, Turin, Italy, in October 2015. He received his M.Sc. degree in Electronic Engineering (Electronic Systems) from Politecnico di Torino, Turin, Italy, in April 2018. Currently, he is Ph.D. Student at Computer Architecture and Robust Energy-Efficient Technologies (CARE-Tech.) lab, Institute of Computer Engineering, Technische Universität Wien (TU Wien), Vienna, Austria, under the supervision of Prof. Dr. Muhammad Shafique. He is also a student IEEE member. His main research interests include hardware and software optimizations for machine learning, brain-inspired computing, VLSI architecture design, emerging computing technologies, robust design, and approximate computing for energy efficiency. He received the honorable mention at the Italian National Finals of Maths Olympic Games in 2012, and the Richard Newton Young Fellow Award in 2019.

Publications

Conference/Symposium Papers

  1. A. Marchisio, G. Pira, M. Martina, G. Masera, M. Shafique, “R-SNN: An Analysis and Design Methodology for Robustifying Spiking Neural Networks against Adversarial Attacks through Noise Filters for Dynamic Vision Sensors, 2021 IEEE/RSJ International Conference on Intelligent Robots and Systems (IROS), September 2021.
  2. A. Marchisio, G. Pira, M. Martina, G. Masera, M. Shafique, DVS-Attacks: Adversarial Attacks on Dynamic Vision Sensors for Spiking Neural NetworksThe International Joint Conference on Neural Networks (IJCNN), at the IEEE World Congress on Computational Intelligence (WCCI), July 2021.
  3. A. Viale, A. Marchisio, M. Martina, G. Masera, M. Shafique, “CarSNN: An Efficient Spiking Neural Network for Event-Based Autonomous Cars on the Loihi Neuromorphic Research Processor”, The International Joint Conference on Neural Networks (IJCNN), at the IEEE World Congress on Computational Intelligence (WCCI), July 2021.
  4. A. Colucci, D. Juhász, M. Mosbeck, A. Marchisio, S. Rehman, M. Kreutzer, G. Nadbath, A. Jantsch, M. Shafique, MLComp: A Methodology for Machine Learning-based Performance Estimation and Adaptive Selection of Pareto-Optimal Compiler Optimization Sequences, IEEE/ACM 24th Design, Automation and Test in Europe Conference (DATE), February, 2021.
  5. R. El-Allami, A. Marchisio, M. Shafique, I. Alouani, Securing Deep Spiking Neural Networks against Adversarial Attacks through Inherent Structural Parameters, IEEE/ACM 24th Design, Automation and Test in Europe Conference (DATE), February, 2021.
  6. A. Marchisio, A. Massa, V. Mrazek, B. Bussolino, M. Martina, M. Shafique, “NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networks”, The IEEE/ACM 2020 International Conference On Computer Aided Design (ICCAD), November 2020.
  7. A. Colucci, A. Marchisio, B. Bussolino, V. Mrazek, M. Martina, G. Masera, M. Shafique. "A Fast Design Space Exploration Framework for the Deep Learning Accelerators: Work-in-Progress", 2020 International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS), September, 2020.
  8. A. Marchisio, B. Bussolino, A. Colucci, M. A. Hanif, M. Martina, G. Masera, M. Shafique, “FasTrCaps: An Integrated Framework for Fast yet Accurate Training of Capsule Networks”, The International Joint Conference on Neural Networks (IJCNN), at the IEEE World Congress on Computational Intelligence (WCCI), July 2020.
  9. R. Massa, A. Marchisio, M. Martina, M. Shafique, “An Efficient Spiking Neural Network for Recognizing Gestures with a DVS Camera on the Loihi Neuromorphic Processor”, The International Joint Conference on Neural Networks (IJCNN), at the IEEE World Congress on Computational Intelligence (WCCI), July 2020.
  10. V. Venceslai, A. Marchisio, I. Alouani, M. Martina, M. Shafique, “NeuroAttack: Undermining Spiking Neural Networks Security through Externally Triggered Bit-Flips”, The International Joint Conference on Neural Networks (IJCNN), at the IEEE World Congress on Computational Intelligence (WCCI), July 2020.
  11. A. Marchisio, G. Nanfa, F. Khalid, M. A. Hanif, M. Martina, M. Shafique, “Is Spiking Secure? A Comparative Study on the Security Vulnerabilities of Spiking and Deep Neural Networks”, The International Joint Conference on Neural Networks (IJCNN), at the IEEE World Congress on Computational Intelligence (WCCI), July 2020.
  12. A. Marchisio, B. Bussolino, A. Colucci, M. Martina, G. Masera, M. Shafique, “Q-CapsNets: A Specialized Framework for Quantizing Capsule Networks”, IEEE/ACM 57th Design Automation Conference (DAC), July 2020.
  13. A. Marchisio, V. Mrazek, M. A. Hanif, M. Shafique, “ReD-CaNe: A Systematic Methodology for Resilience Analysis and Design of Capsule Networks under Approximations”, IEEE/ACM 23rd Design, Automation and Test in Europe Conference (DATE), March 2020.
  14. A. Marchisio, M. A. Hanif, F. Khalid, G. Plastiras, C. Kyrkou, T. Theocharides, M. Shafique, “Deep Learning for Edge Computing: Current Trends, Cross-Layer Optimizations, and Open Research Challenges”, IEEE/ACM International Symposium on VLSI (ISVLSI), July 2019.
  15. A. Marchisio, M. A. Hanif, and M. Shafique, “CapsAcc: An Efficient Hardware Accelerator for CapsuleNets with Data Reuse”, IEEE/ACM 22nd Design, Automation and Test in Europe Conference (DATE), March 2019.
  16. A. Marchisio, M. A. Hanif, M. Martina, and M. Shafique, “PruNet: Class-Blind Pruning Method for Deep Neural Networks”, The International Joint Conference on Neural Networks (IJCNN), July 2018.

Journal Papers

  1. A. Marchisio, V. Mrazek, M. A. Hanif, M. Shafique, “FEECA: Design Space Exploration for Low-Latency and Energy-Efficient Capsule Network Accelerators” in IEEE Transactions on Very Large Scale Integration (VLSI) Systems, 2021.
  2. M. Capra, B. Bussolino, A. Marchisio, G. Masera, M. Martina, M. Shafique. “Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead”, IEEE Access, 2020.
  3. A. Marchisio, V. Mrazek, M. A. Hanif, M. Shafique. “DESCNet: Developing Efficient Scratchpad Memories for Capsule Network Hardware”, IEEE Transactions on Computer-Aided Design of Integrated Circuits and Systems, 2020.
  4. M. Capra, B. Bussolino, A. Marchisio, M. Shafique, G. Masera, and M. Martina. “An Updated Survey of Efficient Hardware Architectures for Accelerating Deep Convolutional Neural Networks”. In MDPI Future Internet, 2020.
  5. M. A. Hanif, A. Marchisio, T. Arif, R. Hafiz, S. Rehman, and M. Shafique, “X-DNNs: Systematic Cross-Layer Approximations for Energy-Efficient Deep Neural Networks”, in ASP Journal of Low Power Electronics (JOLPE), Vol. 14, N° 4, December 2018 - Special Issue on "Machine Learning and Artificial Intelligence in Low Power Electronics".

Workshop Papers

  1. A. Marchisio, G. Nanfa, M. Martina, M. Shafique, “Security Vulnerabilities of Deep, Capsule and Spiking Neural Networks against Adversarial Attacks”, at the IEEE International Workshop on Robust and Trustworthy Machine Learning (RTML), November 2019.
  2. A. Marchisio, G. Nanfa, F. Khalid, M. A. Hanif, M. Martina, M. Shafique, “CapsAttacks: Robust and Imperceptible Adversarial Attacks on Capsule Networks”, at ICML 2019 Workshop on Uncertainty & Robustness in Deep Learning (UDL), at the 36th International Conference on Machine Learning (ICML), Long Beach, CA, USA, June 2019.
  3. A. Marchisio, R. V. W. Putra, M. A. Hanif, and M. Shafique, “HW/SW Co-Design and Co-Optimizations for Deep Learning”, Workshop on INTelligent Embedded Systems Architectures and Applications (INTESA), at the Embedded Systems Week (ESWeek), October 2018.

CS Archive Publications

  1. A. Marchisio, B. Bussolino, A. Colucci, M. A. Hanif, M. Martina, G. Masera, and M, Shafique, “X-TrainCaps: Accelerated Training of Capsule Nets through Lightweight Software Optimizations”, CoRR abs/1905.10142, May 2019.
  2. A. Marchisio, M. A. Hanif, M. T. Teimoori, and M. Shafique, “CapStore: Energy-Efficient Design and Management of the On-Chip Memory for CapsuleNet Inference Accelerators”, CoRR abs/1902.01151, February 2019.
  3. A. Marchisio, G. Nanfa, F. Khalid, M. A. Hanif, M. Martina, and M. Shafique, “SNN under Attack: are Spiking Deep Belief Networks vulnerable to Adversarial Examples?”, CoRR abs/1902.01147, February 2019.
  4. A. Marchisio, G. Nanfa, F. Khalid, M. A. Hanif, M. Martina, and M. Shafique, “CapsAttacks: Robust and Imperceptible Adversarial Attacks on Capsule Networks”, CoRR abs/1901.09878, January 2019.
  5. A. Marchisio, M. A. Hanif, S. Rehman, M. Martina, and M. Shafique, “A Methodology for Automatic Selection of Activation Functions to Design Hybrid Deep Neural Networks”, CoRR abs/1811.03980, October 2018.

Master Thesis

  1. Alberto Marchisio, “Optimizing Deep Neural Networks on GPUs”. Joint supervision of Prof. Dr. Maurizio Martina, Polytechnic University of Turin, and Prof. Dr. Muhammad Shafique, Vienna University of Technology. April 2018.