11 citations · 39 across the 25 of their papers we have counts for
10 papers · 1 filter
Open-Source Heterogeneous SoCs for AI: The PULP Platform Experience
Francesco Conti, Angelo Garofalo, Davide Rossi +2
Since 2013, the PULP (Parallel Ultra-Low Power) Platform project has been one of the most active and successful initiatives in designing research IPs and releasing them as open-sou…
A Flexible Template for Edge Generative AI with High-Accuracy Accelerated Softmax & GELU
Andrea Belano, Yvan Tortorella, Angelo Garofalo +3
Transformer-based generative Artificial Intelligence (GenAI) models achieve remarkable results in a wide range of fields, including natural language processing, computer vision, an…
Distributed Inference with Minimal Off-Chip Traffic for Transformers on Low-Power MCUs
Severin Bochem, Victor J. B. Jung, Arpan Prasad +2
Contextual Artificial Intelligence (AI) based on emerging Transformer models is predicted to drive the next technology revolution in interactive wearable devices such as new-genera…
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…
Distilling Tiny and Ultra-fast Deep Neural Networks for Autonomous Navigation on Nano-UAVs
Lorenzo Lamberti, Lorenzo Bellone, Luka Macan +4
Nano-sized unmanned aerial vehicles (UAVs) are ideal candidates for flying Internet-of-Things smart sensors to collect information in narrow spaces. This requires ultra-fast naviga…
Compressed Latent Replays for Lightweight Continual Learning on Spiking Neural Networks
Alberto Dequino, Alessio Carpegna, Davide Nadalini +4
Rehearsal-based Continual Learning (CL) has been intensely investigated in Deep Neural Networks (DNNs). However, its application in Spiking Neural Networks (SNNs) has not been expl…
HTVM: Efficient Neural Network Deployment On Heterogeneous TinyML Platforms
Josse Van Delm, Maarten Vandersteegen, Alessio Burrello +5
Optimal deployment of deep neural networks (DNNs) on state-of-the-art Systems-on-Chips (SoCs) is crucial for tiny machine learning (TinyML) at the edge. The complexity of these SoC…
Combining Local and Global Perception for Autonomous Navigation on Nano-UAVs
Lorenzo Lamberti, Georg Rutishauser, Francesco Conti +1
A critical challenge in deploying unmanned aerial vehicles (UAVs) for autonomous tasks is their ability to navigate in an unknown environment. This paper introduces a novel vision-…