42 citations · 77 across the 13 of their papers we have counts for
6 papers · 1 filter
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…
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…
Optimizing Layer-Fused Scheduling of Transformer Networks on Multi-accelerator Platforms
Steven Colleman, Arne Symons, Victor J. B. Jung +1
The impact of transformer networks is booming, yet, they come with significant computational complexity. It is therefore essential to understand how to optimally map and execute th…
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…
A Tiny Transformer for Low-Power Arrhythmia Classification on Microcontrollers
Paola Busia, Matteo Antonio Scrugli, Victor Jean-Baptiste Jung +2
Wearable systems for the continuous and real-time monitoring of cardiovascular diseases are becoming widespread and valuable assets in diagnosis and therapy. A promising approach f…