34 citations · 56 across the 11 of their papers we have counts for
6 papers · 1 filter
xTern: Energy-Efficient Ternary Neural Network Inference on RISC-V-Based Edge Systems
Georg Rutishauser, Joan Mihali, Moritz Scherer +1
Ternary neural networks (TNNs) offer a superior accuracy-energy trade-off compared to binary neural networks. However, until now, they have required specialized accelerators to rea…
A 3 TOPS/W RISC-V Parallel Cluster for Inference of Fine-Grain Mixed-Precision Quantized Neural Networks
Alessandro Nadalini, Georg Rutishauser, Alessio Burrello +5
The emerging trend of deploying complex algorithms, such as Deep Neural Networks (DNNs), increasingly poses strict memory and energy efficiency requirements on Internet-of-Things (…
Marsellus: A Heterogeneous RISC-V AI-IoT End-Node SoC with 2-to-8b DNN Acceleration and 30%-Boost Adaptive Body Biasing
Francesco Conti, Gianna Paulin, Angelo Garofalo +7
Emerging Artificial Intelligence-enabled Internet-of-Things (AI-IoT) System-on-a-Chip (SoC) for augmented reality, personalized healthcare, and nano-robotics need to run many diver…
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 +3
End-to-end event-based computation has the potential to push the envelope in latency and energy efficiency for edge AI applications. Unfortunately, event-based sensors (e.g., DVS c…
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, Alfio Di Mauro, Tim Fischer +2
Tiny Machine Learning (TinyML) applications impose uJ/Inference constraints, with a maximum power consumption of tens of mW. It is extremely challenging to meet these requirements…
CUTIE: Beyond PetaOp/s/W Ternary DNN Inference Acceleration with Better-than-Binary Energy Efficiency
Moritz Scherer, Georg Rutishauser, Lukas Cavigelli +1
We present a 3.1 POp/s/W fully digital hardware accelerator for ternary neural networks. CUTIE, the Completely Unrolled Ternary Inference Engine, focuses on minimizing non-computat…