G Rutishauser

Flexible and Fully Quantized Ultra-Lightweight TinyissimoYOLO for Ultra-Low-Power Edge Systems

Julian Moosmann, Hanna Mueller, Nicky Zimmerman, G Rutishauser et autres

This paper deploys and explores variants of TinyissimoYOLO, a highly flexible and fully quantized ultra-lightweight object detection network designed for edge systems with a power envelope of a few milliwatts. With experimental measurements, we present a comprehensive characterization of the network's detection …

ch, it (code pays fourni par la source)

ColibriUAV: An Ultra-Fast, Energy-Efficient Neuromorphic Edge Processing UAV-Platform with Event-Based and Frame-Based Cameras

Sizhen Bian, Lukas Schulthess, G Rutishauser, Alfio Di Mauro et autres

The interest in dynamic vision sensor (DVS)-powered unmanned aerial vehicles (UAV) is raising, especially due to the microsecond-level reaction time of the bio-inspired event sensor, which increases robustness and reduces latency of the perception tasks compared to a RGB camera. This work …

ch (code pays fourni par la source)

ColibriUAV: An Ultra-Fast, Energy-Efficient Neuromorphic Edge Processing UAV-Platform with Event-Based and Frame-Based Cameras

Sizhen Bian, Lukas Schulthess, G Rutishauser, Alfio Di Mauro et autres

The interest in dynamic vision sensor (DVS)-powered unmanned aerial vehicles (UAV) is raising, especially due to the microsecond-level reaction time of the bio-inspired event sensor, which increases robustness and reduces latency of the perception tasks compared to a RGB camera. This work …

ColibriES: A Milliwatts RISC-V Based Embedded System Leveraging Neuromorphic and Neural Networks Hardware Accelerators for Low-Latency Closed-loop Control Applications

G Rutishauser, Robin Hunziker, Alfio Di Mauro, Sizhen Bian et autres

End-to-end event-based computation has the poten-tial to push the envelope in latency and energy efficiency for edge AI applications. Unfortunately, event-based sensors (e.g., DVS cameras) and neuromorphic spike-based processors (e.g., Loihi) have been designed in a decoupled fashion, thereby missing major streamlining …

ch (code pays fourni par la source)

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, Davide Rossi et autres

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 diverse tasks within a power envelope of a few tens of mW over a wide range of operating conditions: compute-intensive but strongly quantized Deep …

sg, ch, ca, it (code pays fourni par la source)

ColibriES: A Milliwatts RISC-V Based Embedded System Leveraging Neuromorphic and Neural Networks Hardware Accelerators for Low-Latency Closed-loop Control Applications

G Rutishauser, Robin Hunziker, Alfio Di Mauro, Sizhen Bian et autres

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 cameras) and neuromorphic spike-based processors (e.g., Loihi) have been designed in a decoupled fashion, thereby missing major streamlining …

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 Sebastian Fischer, G Rutishauser et autres

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 at a reasonable accuracy level. This work addresses the challenge with a flexible, fully digital Ternary Neural …

Ternarized TCN for $\mu \mathrm{J}/\text{Inference}$ Gesture Recognition from DVS Event Frames

G Rutishauser, Moritz Scherer, Tim Fischer, Luca Benini

Dynamic Vision Sensors (DVS) offer the opportunity to scale the energy consumption in image acquisition proportionally to the activity in the captured scene by only transmitting data when the captured image changes. Their potential for energy-proportional sensing makes them highly attractive for …

ch (code pays fourni par la source)

CUTIE: Beyond PetaOp/s/W Ternary DNN Inference Acceleration with\n Better-than-Binary Energy Efficiency

Moritz Scherer, G Rutishauser, Lukas Cavigelli, Luca Benini

We present a 3.1 POp/s/W fully digital hardware accelerator for ternary\nneural networks. CUTIE, the Completely Unrolled Ternary Inference Engine,\nfocuses on minimizing non-computational energy and switching activity so that\ndynamic power spent on storing (locally or globally) intermediate results is\nminimized. This is achieved by …

EBPC: Extended Bit-Plane Compression for Deep Neural Network Inference\n and Training Accelerators

Lukas Cavigelli, G Rutishauser, Luca Benini

In the wake of the success of convolutional neural networks in image\nclassification, object recognition, speech recognition, etc., the demand for\ndeploying these compute-intensive ML models on embedded and mobile systems with\ntight power and energy constraints at low cost, as well as for boosting\nthroughput …

Transurethral Resection of Prostatic Adenoma in Basel

G Rutishauser

Extremitätenthrombose und Lungenembolie bei internen Krankheiten

G Rutishauser