7 citations · 8 across the 5 of their papers we have counts for
5 papers
TASTE: Throughput-Aware Batch Size Tuning for On-Device Edge Learning
Avik Bhatnagar, Federico Nicolas Peccia, Oliver Bringmann
The rise of privacy-preserving artificial intelligence (AI) has shifted the focus of model adaptation and personalization towards on-device learning, where deep learning models are…
Tensor Program Optimization for the RISC-V Vector Extension Using Probabilistic Programs
Federico Nicolas Peccia, Frederik Haxel, Oliver Bringmann
RISC-V provides a flexible and scalable platform for applications ranging from embedded devices to high-performance computing clusters. Particularly, its RISC-V Vector Extension (R…
Efficient Edge AI: Deploying Convolutional Neural Networks on FPGA with the Gemmini Accelerator
Federico Nicolas Peccia, Svetlana Pavlitska, Tobias Fleck +1
The growing concerns regarding energy consumption and privacy have prompted the development of AI solutions deployable on the edge, circumventing the substantial CO2 emissions asso…
Embedded Distributed Inference of Deep Neural Networks: A Systematic Review
Federico Nicolás Peccia, Oliver Bringmann
Embedded distributed inference of Neural Networks has emerged as a promising approach for deploying machine-learning models on resource-constrained devices in an efficient and scal…
Integration of a systolic array based hardware accelerator into a DNN operator auto-tuning framework
F. N. Peccia, O. Bringmann
The deployment of neural networks on heterogeneous SoCs coupled with custom accelerators is a challenging task because of the lack of end-to-end software tools provided for these s…