| An updated survey of efficient hardware architectures for accelerating deep convolutional neural networks M Capra, B Bussolino, A Marchisio, M Shafique, G Masera, M Martina Future Internet 12 (7), 113, 2020 | 174 | 2020 |
| Hardware and software optimizations for accelerating deep neural networks: Survey of current trends, challenges, and the road ahead M Capra, B Bussolino, A Marchisio, G Masera, M Martina, M Shafique IEEE Access 8, 225134-225180, 2020 | 160 | 2020 |
| NASCaps: A framework for neural architecture search to optimize the accuracy and hardware efficiency of convolutional capsule networks A Marchisio, A Massa, V Mrazek, B Bussolino, M Martina, M Shafique Proceedings of the 39th International Conference on Computer-Aided Design, 1-9, 2020 | 46 | 2020 |
| Q-capsnets: A specialized framework for quantizing capsule networks A Marchisio, B Bussolino, A Colucci, M Martina, G Masera, M Shafique 2020 57th ACM/IEEE Design Automation Conference (DAC), 1-6, 2020 | 18 | 2020 |
| FasTrCaps: An integrated framework for fast yet accurate training of capsule networks A Marchisio, B Bussolino, A Colucci, MA Hanif, M Martina, G Masera, ... 2020 International Joint Conference on Neural Networks (IJCNN), 1-8, 2020 | 11 | 2020 |
| X-traincaps: Accelerated training of capsule nets through lightweight software optimizations A Marchisio, B Bussolino, A Colucci, MA Hanif, M Martina, G Masera, ... arXiv preprint arXiv:1905.10142, 2019 | 11 | 2019 |
| Going Further With Winograd Convolutions: Tap-Wise Quantization for Efficient Inference on 4x4 Tiles R Andri, B Bussolino, A Cipolletta, L Cavigelli, Z Wang 2022 55th IEEE/ACM International Symposium on Microarchitecture (MICRO), 582-598, 2022 | 7 | 2022 |
| Enabling Capsule Networks at the Edge through Approximate Softmax and Squash Operations A Marchisio, B Bussolino, E Salvati, M Martina, G Masera, M Shafique Proceedings of the ACM/IEEE International Symposium on Low Power Electronics …, 2022 | 7 | 2022 |
| Rohnas: A neural architecture search framework with conjoint optimization for adversarial robustness and hardware efficiency of convolutional and capsule networks A Marchisio, V Mrazek, A Massa, B Bussolino, M Martina, M Shafique IEEE Access 10, 109043-109055, 2022 | 6 | 2022 |
| A Fast Design Space Exploration Framework for the Deep Learning Accelerators: Work-in-Progress A Colucci, A Marchisio, B Bussolino, V Mrazek, M Martina, G Masera, ... 2020 International Conference on Hardware/Software Codesign and System …, 2020 | 3 | 2020 |
| 11.3 Metis AIPU: A 12nm 15TOPS/W 209.6 TOPS SoC for Cost-and Energy-Efficient Inference at the Edge PA Hager, B Moons, S Cosemans, IA Papistas, B Rooseleer, J Van Loon, ... 2024 IEEE International Solid-State Circuits Conference (ISSCC) 67, 212-214, 2024 | | 2024 |
| Hardware and Software Optimizations for Capsule Networks A Marchisio, B Bussolino, A Colucci, V Mrazek, MA Hanif, M Martina, ... Embedded Machine Learning for Cyber-Physical, IoT, and Edge Computing …, 2023 | | 2023 |
| Techniques and Optimization Strategies for Efficient Hardware Acceleration of Neural Networks: Tap-Wisely-Quantized Winograd Algorithm and Capsule Networks B Bussolino Politecnico di Torino, 2023 | | 2023 |
| NLCMAP: A Framework for the Efficient Mapping of Non-Linear Convolutional Neural Networks on FPGA Accelerators G Aiello, B Bussolino, E Valpreda, MR Roch, G Masera, M Martina, ... 2022 IEEE International Conference on Image Processing (ICIP), 926-930, 2022 | | 2022 |
| Going Further With Winograd Convolutions: Tap-Wise Quantization for Efficient Inference on 4x4 Tile A Renzo, B Bussolino, C Lukas, W Zhe MICRO-55: 55th Annual IEEE/ACM International Symposium on Microarchitecture …, 2022 | | 2022 |
| Capsule Networks: Training and Quantized Inference B Bussolino Politecnico di Torino, 2019 | | 2019 |
| HARNAS: Neural Architecture Search Jointly Optimizing for Hardware Efficiency and Adversarial Robustness of Convolutional and Capsule Networks A Marchisio, V Mrazek, A Massa, B Bussolino, M Martina, M Shafique | | |
