Hardware accelerators for Deep Neural Networks training and inference

Corso di laurea magistrale

• Integrated systems architecture. A.A. 2020/21, INGEGNERIA ELETTRONICA (ELECTRONIC ENGINEERING). Collaboratore del corso

Corso di laurea di 1° livello

• Elettronica applicata. A.A. 2020/21, INGEGNERIA ELETTRONICA. Collaboratore del corso
• Elettronica applicata. A.A. 2021/22, INGEGNERIA ELETTRONICA. Collaboratore del corso

• Bussolino, Beatrice (2023)
  Techniques and Optimization Strategies for Efficient Hardware Acceleration of Neural Networks: Tap-Wisely-Quantized Winograd Algorithm and Capsule Networks. relatore: MARTINA, MAURIZIO; , 35. XXXV Ciclo, P.: 167
  Doctoral Thesis
• Marchisio, Alberto; Bussolino, Beatrice; Salvati, Edoardo; Martina, Maurizio; Masera, ... (2022)
  Enabling Capsule Networks at the Edge through Approximate Softmax and Squash Operations. In: ISLPED '22: ACM/IEEE International Symposium on Low Power Electronics and Design, Boston (USA), 1-2 August 2022. ISBN: 9781450393546
  Contributo in Atti di Convegno (Proceeding)
• Andri, Renzo; Bussolino, Beatrice; Cipolletta, Antonio; Cavigelli, Lukas; Wang, Zhe (2022)
  Going Further With Winograd Convolutions: Tap-Wise Quantization for Efficient Inference on 4x4 Tile. In: IEEE/ACM International Symposium on Microarchitecture, Chicago (USA), 1-5 October 2022, pp. 582-598. ISBN: 978-1-6654-6272-3
  Contributo in Atti di Convegno (Proceeding)
• Marchisio, Alberto; Mrazek, Vojtech; Massa, Andrea; Bussolino, Beatrice; Martina, ... (2022)
  RoHNAS: A Neural Architecture Search Framework with Conjoint Optimization for Adversarial Robustness and Hardware Efficiency of Convolutional and Capsule Networks. In: IEEE ACCESS, vol. 10, pp. 109043-109055. ISSN 2169-3536
  Contributo su Rivista
• Aiello, Giuseppe; Bussolino, Beatrice; Valpreda, Emanuele; Ruo Roch, Massimo; Masera, ... (2022)
  NLCMAP: A Framework for the Efficient Mapping of Non-Linear Convolutional Neural Networks on FPGA Accelerators. In: International Conference on Image Processing, Bordeaux, France, 16-19 October 2022. ISBN: 978-1-6654-9620-9
  Contributo in Atti di Convegno (Proceeding)
• Colucci, Alessio; Marchisio, Alberto; Bussolino, Beatrice; Mrazek, Voitech; Martina, ... (2020)
  A Fast Design Space Exploration Framework for the Deep Learning Accelerators: Work-in-Progress. In: International Conference on Hardware/Software Codesign and System Synthesis (CODES+ISSS), Singapore, 20-25 September 2020, pp. 34-36. ISBN: 978-1-7281-9198-0
  Contributo in Atti di Convegno (Proceeding)
• Marchisio, Alberto; Bussolino, Beatrice; Colucci, Alessio; Hanif, Muhammad Abdullah; ... (2020)
  FasTrCaps: An Integrated Framework for Fast yet Accurate Training of Capsule Networks. In: 2020 International Joint Conference on Neural Networks (IJCNN), Glasgow (UK), 19-24 July 2020, pp. 1-8. ISBN: 978-1-7281-6926-2
  Contributo in Atti di Convegno (Proceeding)
• Marchisio, Alberto; Massa, Andrea; Mrazek, Vojtech; Bussolino, Beatrice; Martina, ... (2020)
  NASCaps: A Framework for Neural Architecture Search to Optimize the Accuracy and Hardware Efficiency of Convolutional Capsule Networks. In: 2020 IEEE/ACM International Conference On Computer Aided Design (ICCAD), Virtual Event, USA, 2 - 5 Novembre, pp. 1-9. ISBN: 9781450380263
  Contributo in Atti di Convegno (Proceeding)
• Capra, M.; Bussolino, B.; Marchisio, A.; Masera, G.; Martina, M.; Shafique, M. (2020)
  Hardware and Software Optimizations for Accelerating Deep Neural Networks: Survey of Current Trends, Challenges, and the Road Ahead. In: IEEE ACCESS, vol. 8, pp. 225134-225180. ISSN 2169-3536
  Contributo su Rivista
• Capra, Maurizio; Bussolino, Beatrice; Marchisio, Alberto; Shafique, Muhammad; Masera, ... (2020)
  An Updated Survey of Efficient Hardware Architectures for Accelerating Deep Convolutional Neural Networks. In: FUTURE INTERNET, vol. 12, pp. 113-134. ISSN 1999-5903
  Contributo su Rivista
• Marchisio, Alberto; Bussolino, Beatrice; Colucci, Alessio; Martina, Maurizio; Masera, ... (2020)
  Q-CapsNets: A Specialized Framework for Quantizing Capsule Networks. In: 2020 57th ACM/IEEE Design Automation Conference (DAC), San Francisco, CA, USA, USA, 20-24 July 2020, pp. 1-6. ISBN: 978-1-7281-1085-1
  Contributo in Atti di Convegno (Proceeding)