10 citations · 13 across the 9 of their papers we have counts for
9 papers
STEEL: Sparsity-Aware Fused Attention for Energy-Efficient Long-Sequence Inference on AMD's XDNA NPU
Victor J. B. Jung, Gagandeep Singh, Joseph Melber +3
The growing adoption of large language model-based agents within operating system workflows has increased the importance of energy-efficient inference on laptop-class systems-on-ch…
BioTrain: Sub-MB, Sub-50mW On-Device Fine-Tuning for Edge-AI on Biosignals
Run Wang, Victor J. B. Jung, Philip Wiese +5
Biosignals exhibit substantial cross-subject and cross-session variability, inducing severe domain shifts that degrade post-deployment performance for small, edge-oriented AI model…
Architecture, Simulation and Software Stack to Support Post-CMOS Accelerators: The ARCHYTAS Project
Giovanni Agosta, Stefano Cherubin, Derek Christ +11
ARCHYTAS aims to design and evaluate non-conventional hardware accelerators, in particular, optoelectronic, volatile and non-volatile processing-in-memory, and neuromorphic, to tac…
Fused-Tiled Layers: Minimizing Data Movement on RISC-V SoCs with Software-Managed Caches
Victor J. B. Jung, Alessio Burrello, Francesco Conti +1
The success of DNNs and their high computational requirements pushed for large codesign efforts aiming at DNN acceleration. Since DNNs can be represented as static computational gr…
Lightweight Software Kernels and Hardware Extensions for Efficient Sparse Deep Neural Networks on Microcontrollers
Francesco Daghero, Daniele Jahier Pagliari, Francesco Conti +3
The acceleration of pruned Deep Neural Networks (DNNs) on edge devices such as Microcontrollers (MCUs) is a challenging task, given the tight area- and power-constraints of these d…
A Reliable, Time-Predictable Heterogeneous SoC for AI-Enhanced Mixed-Criticality Edge Applications
Angelo Garofalo, Alessandro Ottaviano, Matteo Perotti +20
Next-generation mixed-criticality Systems-on-chip (SoCs) for robotics, automotive, and space must execute mixed-criticality AI-enhanced sensor processing and control workloads, ens…
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…
A Survey on Design Methodologies for Accelerating Deep Learning on Heterogeneous Architectures
Serena Curzel, Fabrizio Ferrandi, Leandro Fiorin +15
Given their increasing size and complexity, the need for efficient execution of deep neural networks has become increasingly pressing in the design of heterogeneous High-Performanc…