24 citations · 35 across the 10 of their papers we have counts for
12 papers
Circuits and Systems for Embodied AI: Exploring uJ Multi-Modal Perception for Nano-UAVs on the Kraken Shield
Viviane Potocnik, Alfio Di Mauro, Lorenzo Lamberti +4
Embodied artificial intelligence (AI) requires pushing complex multi-modal models to the extreme edge for time-constrained tasks such as autonomous navigation of robots and vehicle…
Deeploy: Enabling Energy-Efficient Deployment of Small Language Models On Heterogeneous Microcontrollers
Moritz Scherer, Luka Macan, Victor Jung +5
With the rise of Embodied Foundation Models (EFMs), most notably Small Language Models (SLMs), adapting Transformers for edge applications has become a very active field of researc…
Toward Attention-based TinyML: A Heterogeneous Accelerated Architecture and Automated Deployment Flow
Philip Wiese, Gamze İslamoğlu, Moritz Scherer +5
One of the challenges for Tiny Machine Learning (tinyML) is keeping up with the evolution of Machine Learning models from Convolutional Neural Networks to Transformers. We address…
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…
Optimizing the Deployment of Tiny Transformers on Low-Power MCUs
Victor J. B. Jung, Alessio Burrello, Moritz Scherer +2
Transformer networks are rapidly becoming SotA in many fields, such as NLP and CV. Similarly to CNN, there is a strong push for deploying Transformer models at the extreme edge, ul…
Siracusa: A 16 nm Heterogenous RISC-V SoC for Extended Reality with At-MRAM Neural Engine
Arpan Suravi Prasad, Moritz Scherer, Francesco Conti +9
Extended reality (XR) applications are Machine Learning (ML)-intensive, featuring deep neural networks (DNNs) with millions of weights, tightly latency-bound (10-20 ms end-to-end),…
ITA: An Energy-Efficient Attention and Softmax Accelerator for Quantized Transformers
Gamze İslamoğlu, Moritz Scherer, Gianna Paulin +4
Transformer networks have emerged as the state-of-the-art approach for natural language processing tasks and are gaining popularity in other domains such as computer vision and aud…
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…
Kraken: A Direct Event/Frame-Based Multi-sensor Fusion SoC for Ultra-Efficient Visual Processing in Nano-UAVs
Alfio Di Mauro, Moritz Scherer, Davide Rossi +1
Small-size unmanned aerial vehicles (UAV) have the potential to dramatically increase safety and reduce cost in applications like critical infrastructure maintenance and post-disas…
Bioformers: Embedding Transformers for Ultra-Low Power sEMG-based Gesture Recognition
Alessio Burrello, Francesco Bianco Morghet, Moritz Scherer +5
Human-machine interaction is gaining traction in rehabilitation tasks, such as controlling prosthetic hands or robotic arms. Gesture recognition exploiting surface electromyographi…
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…
TinyRadarNN: Combining Spatial and Temporal Convolutional Neural Networks for Embedded Gesture Recognition with Short Range Radars
Moritz Scherer, Michele Magno, Jonas Erb +3
This work proposes a low-power high-accuracy embedded hand-gesture recognition algorithm targeting battery-operated wearable devices using low power short-range RADAR sensors. A 2D…