EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference and Training Accelerators. - Lukas Cavigelli; Georg Rutishauser; Luca Benini (IEEE J. Emerg. Sel. Topics Circuits Syst., 2019)

A 1036 TOp/s/W, 12.2 mW, 2.72 μJ/Inference All Digital TNN Accelerator in 22 nm FDX Technology for TinyML Applications. - Moritz Scherer 001; Alfio Di Mauro; Georg Rutishauser; Tim Fischer 001; Luca Benini (COOL CHIPS, 2022)

Ternarized TCN for $ mu mathrm{J}/ text{Inference}$ Gesture Recognition from DVS Event Frames. - Georg Rutishauser; Moritz Scherer 001; Tim Fischer 001; Luca Benini (DATE, 2022)

TCN-CUTIE: A 1036 TOp/s/W, 2.72 uJ/Inference, 12.2 mW All-Digital Ternary Accelerator in 22 nm FDX Technology. - Moritz Scherer 001; Alfio Di Mauro; Tim Fischer 001; Georg Rutishauser; Luca Benini (CoRR, 2022)

CUTIE: Beyond PetaOp/s/W Ternary DNN Inference Acceleration With Better-Than-Binary Energy Efficiency. - Moritz Scherer 001; Georg Rutishauser; Lukas Cavigelli; Luca Benini (IEEE Trans. Comput. Aided Des. Integr. Circuits Syst., 2022)

Free Bits: Latency Optimization of Mixed-Precision Quantized Neural Networks on the Edge. - Georg Rutishauser; Francesco Conti 001; Luca Benini (AICAS, 2023)

ColibriES: A Milliwatts RISC-V Based Embedded System Leveraging Neuromorphic and Neural Networks Hardware Accelerators for Low-Latency Closed-loop Control Applications. - Georg Rutishauser; Robin Hunziker; Alfio Di Mauro; Sizhen Bian; Luca Benini; Michele Magno (ISCAS, 2023)

A 12.4TOPS/W @ 136GOPS AI-IoT System-on-Chip with 16 RISC-V, 2-to-8b Precision-Scalable DNN Acceleration and 30%-Boost Adaptive Body Biasing. - Francesco Conti 001; Davide Rossi; Gianna Paulin; Angelo Garofalo; Alfio Di Mauro; Georg Rutishauser; Gianmarco Ottavi; Manuel Eggimann; Hayate Okuhara; Vincent Huard; Olivier Montfort; Lionel Jure; et al. (ISSCC, 2023)

A 3 TOPS/W RISC-V Parallel Cluster for Inference of Fine-Grain Mixed-Precision Quantized Neural Networks. - Alessandro Nadalini; Georg Rutishauser; Alessio Burrello; Nazareno Bruschi; Angelo Garofalo; Luca Benini; Francesco Conti 001; Davide Rossi (ISVLSI, 2023)

ColibriUAV: An Ultra-Fast, Energy-Efficient Neuromorphic Edge Processing UAV-Platform with Event-Based and Frame-Based Cameras. - Sizhen Bian; Lukas Schulthess; Georg Rutishauser; Alfio Di Mauro; Luca Benini; Michele Magno (IWASI, 2023)

Marsellus: A Heterogeneous RISC-V AI-IoT End-Node SoC with 2-to-8b DNN Acceleration and 30%-Boost Adaptive Body Biasing. - Francesco Conti 001; Gianna Paulin; Davide Rossi; Alfio Di Mauro; Georg Rutishauser; Gianmarco Ottavi; Manuel Eggimann; Hayate Okuhara; Luca Benini (CoRR, 2023)

Flexible and Fully Quantized Ultra-Lightweight TinyissimoYOLO for Ultra-Low-Power Edge Systems. - Julian Moosmann; Hanna Müller; Nicky Zimmerman; Georg Rutishauser; Luca Benini; Michele Magno (CoRR, 2023)

TCN-CUTIE: A 1, 036-TOp/s/W, 2.72-µJ/Inference, 12.2-mW All-Digital Ternary Accelerator in 22-nm FDX Technology. - Moritz Scherer 001; Alfio Di Mauro; Tim Fischer 001; Georg Rutishauser; Luca Benini (IEEE Micro, 2023)

7 μJ/inference end-to-end gesture recognition from dynamic vision sensor data using ternarized hybrid convolutional neural networks. - Georg Rutishauser; Moritz Scherer 001; Tim Fischer 001; Luca Benini (Future Gener. Comput. Syst., 2023)

xTern: Energy-Efficient Ternary Neural Network Inference on RISC-V-Based Edge Systems. - Georg Rutishauser; Joan Mihali; Moritz Scherer 001; Luca Benini (ASAP, 2024)

Combining Local and Global Perception for Autonomous Navigation on Nano-UAVs. - Lorenzo Lamberti; Georg Rutishauser; Francesco Conti 001; Luca Benini (ERF (1), 2024)

Flexible and Fully Quantized Lightweight TinyissimoYOLO for Ultra-Low-Power Edge Systems. - Julian Moosmann; Hanna Müller; Nicky Zimmerman; Georg Rutishauser; Luca Benini; Michele Magno (IEEE Access, 2024)

Marsellus: A Heterogeneous RISC-V AI-IoT End-Node SoC With 2-8 b DNN Acceleration and 30%-Boost Adaptive Body Biasing. - Francesco Conti 001; Gianna Paulin; Angelo Garofalo; Davide Rossi; Alfio Di Mauro; Georg Rutishauser; Gianmarco Ottavi; Manuel Eggimann; Hayate Okuhara; Luca Benini (IEEE J. Solid State Circuits, 2024)